• Energy Research

These datasets were prepared within the scope of the EJP SOIL programme. The datasets are extracted from different sources, clipped and reprojected to EPSG:3035. The sources are listed in the table below. The datasets were used as environmental layers to prodict soil property distribution (soil maps) at National and continental level within the EJP SOIL programme. Dataset sources: Copernicus Climate Data Store https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview RESOLVE Biodiversity and Wildlife Solutions https://ecoregions2017.appspot.com/ Copernicus Land Monitoring Service https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/ European Union/ESA/Copernicus https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/ GLiM - Global Lithological Map https://www.geo.uni-hamburg.de/en/geologie/forschung/aquatische-geochemie/glim.html

Soil organic matter is essential for preserving and maintaining a range of soil and ecosystem functions as well as supply and store carbon for climate change mitigation. Digital Soil Mapping techniques will be used to obtain a spatially continuous product, especially over permanently vegetated areas. Recently available satellite remote sensing data, with among other systems the Copernicus Sentinel, will be used as input for environmental covariates. Digital Soil Mapping, coupled together with Remote Sensing products, is a powerful tool to produce soil properties maps and monitoring the changes in soil conditions over time.

The ‘4 per mille Soils for Food Security and Climate’ was launched at the COP21 with an aspiration to increase global soil organic matter stocks by 4 per 1000 (or 0.4 %) per year as a compensation for the global emissions of greenhouse gases by anthropogenic sources. This paper surveyed the soil organic carbon (SOC) stock estimates and sequestration potentials from 20 regions in the world (New Zealand, Chile, South Africa, Australia, Tanzania, Indonesia, Kenya, Nigeria, India, China Taiwan, South Korea, China Mainland, United States of America, France, Canada, Belgium, England & Wales, Ireland, Scotland, and Russia). We asked whether the 4 per mille initiative is feasible for the region. The outcomes highlight region specific efforts and scopes for soil carbon sequestration. Reported soil C sequestration rates globally show that under best management practices, 4 per mille or even higher sequestration rates can be accomplished. High C sequestration rates (up to 10 per mille) can be achieved for soils with low initial SOC stock (topsoil less than 30 t C ha− 1), and at the first twenty years after implementation of best management practices. In addition, areas which have reached equilibrium will not be able to further increase their sequestration. We found that most studies on SOC sequestration only consider topsoil (up to 0.3 m depth), as it is considered to be most affected by management techniques. The 4 per mille number was based on a blanket calculation of the whole global soil profile C stock, however the potential to increase SOC is mostly on managed agricultural lands. If we consider 4 per mille in the top 1m of global agricultural soils, SOC sequestration is between 2-3 Gt C year− 1, which effectively offset 20–35% of global anthropogenic greenhouse gas emissions. As a strategy for climate change mitigation, soil carbon sequestration buys time over the next ten to twenty years while other effective sequestration and low carbon technologies become viable. The challenge for cropping farmers is to find disruptive technologies that will further improve soil condition and deliver increased soil carbon. Progress in 4 per mille requires collaboration and communication between scientists, farmers, policy makers, and marketeers.

Pour mieux comprendre l'application mondiale des modèles de prédiction de l'érosion des sols, nous avons examiné de manière approfondie la littérature de recherche pertinente évaluée par des pairs sur la modélisation de l'érosion des sols publiée entre 1994 et 2017. Nous avons cherché à identifier (i) les processus et les modèles les plus fréquemment abordés dans la littérature, (ii) les régions dans lesquelles les modèles sont principalement appliqués, (iii) les régions qui restent non traitées et pourquoi, et (iv) la fréquence des études menées pour valider/évaluer les résultats des modèles par rapport aux données mesurées. Pour mener à bien cette tâche, nous avons combiné les connaissances collectives de 67 scientifiques spécialistes de l'érosion des sols de 25 pays. La base de données résultante, intitulée « Global Applications of Soil Erosion Modelling Tracker (GASEMT) », comprend 3030 enregistrements de modélisation individuels provenant de 126 pays, englobant tous les continents (à l'exception de l'Antarctique). Sur les 8471 articles identifiés comme potentiellement pertinents, nous avons examiné 1697 articles appropriés et systématiquement évalué et transféré 42 attributs pertinents dans la base de données. Cette base de données GASEMT fournit des informations complètes sur l'état de l'art des modèles d'érosion des sols et des applications de modèles dans le monde entier. Cette base de données vise à soutenir la prochaine évaluation mondiale de l'érosion des sols par les Nations Unies basée sur les pays, en plus d'aider à éclairer les priorités de recherche sur l'érosion des sols en construisant une base pour de futures analyses ciblées et approfondies. GASEMT est une base de données open-source à la disposition de l'ensemble de la communauté des utilisateurs pour développer la recherche, corriger les erreurs et faire des extensions futures. Para comprender mejor la aplicación global de los modelos de predicción de la erosión del suelo, revisamos exhaustivamente la literatura de investigación relevante revisada por pares sobre modelos de erosión del suelo publicada entre 1994 y 2017. Nuestro objetivo fue identificar (i) los procesos y modelos abordados con mayor frecuencia en la literatura, (ii) las regiones dentro de las cuales se aplican principalmente los modelos, (iii) las regiones que permanecen sin abordar y por qué, y (iv) con qué frecuencia se realizan estudios para validar/evaluar los resultados del modelo en relación con los datos medidos. Para realizar esta tarea, combinamos el conocimiento colectivo de 67 científicos de erosión de suelos de 25 países. La base de datos resultante, denominada 'Global Applications of Soil Erosion Modelling Tracker (GASEMT)', incluye 3030 registros de modelado individuales de 126 países, que abarcan todos los continentes (excepto la Antártida). De los 8471 artículos identificados como potencialmente relevantes, revisamos 1697 artículos apropiados y evaluamos y transferimos sistemáticamente 42 atributos relevantes a la base de datos. Esta base de datos GASEMT proporciona información integral sobre el estado del arte de los modelos de erosión del suelo y las aplicaciones de modelos en todo el mundo. Esta base de datos tiene la intención de apoyar la próxima evaluación mundial de la erosión del suelo de las Naciones Unidas basada en los países, además de ayudar a informar las prioridades de investigación de la erosión del suelo mediante la creación de una base para futuros análisis específicos y en profundidad. GASEMT es una base de datos de código abierto disponible para toda la comunidad de usuarios para desarrollar investigaciones, rectificar errores y realizar futuras expansiones. To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named 'Global Applications of Soil Erosion Modelling Tracker (GASEMT)', includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions. للحصول على فهم أفضل للتطبيق العالمي لنماذج التنبؤ بتآكل التربة، قمنا بمراجعة شاملة للأدبيات البحثية ذات الصلة التي استعرضها الأقران حول نمذجة تآكل التربة المنشورة بين عامي 1994 و 2017. كنا نهدف إلى تحديد (1) العمليات والنماذج التي يتم تناولها بشكل متكرر في الأدبيات، (2) المناطق التي يتم فيها تطبيق النماذج في المقام الأول، (3) المناطق التي لا تزال دون معالجة ولماذا، و (4) عدد المرات التي يتم فيها إجراء دراسات للتحقق من صحة/تقييم نتائج النموذج بالنسبة للبيانات المقاسة. لأداء هذه المهمة، جمعنا المعرفة الجماعية لـ 67 عالمًا في مجال تآكل التربة من 25 دولة. تتضمن قاعدة البيانات الناتجة، المسماة "التطبيقات العالمية لتتبع نمذجة تآكل التربة (GASEMT )"، 3030 سجل نمذجة فردي من 126 دولة، تشمل جميع القارات (باستثناء القارة القطبية الجنوبية). من بين 8471 مقالة تم تحديدها على أنها ذات صلة محتملة، قمنا بمراجعة 1697 مقالة مناسبة وقمنا بتقييم ونقل 42 سمة ذات صلة بشكل منهجي إلى قاعدة البيانات. توفر قاعدة بيانات GASEMT هذه رؤى شاملة حول أحدث نماذج تآكل التربة وتطبيقات النماذج في جميع أنحاء العالم. تهدف قاعدة البيانات هذه إلى دعم التقييم العالمي المقبل لتآكل التربة الذي تجريه الأمم المتحدة على المستوى القطري بالإضافة إلى المساعدة في توجيه أولويات أبحاث تآكل التربة من خلال بناء أساس للتحليلات المتعمقة المستهدفة في المستقبل. GASEMT هي قاعدة بيانات مفتوحة المصدر متاحة لمجتمع المستخدمين بأكمله لتطوير البحث وتصحيح الأخطاء وإجراء التوسعات المستقبلية.

AbstractSpatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate change mitigation. In this work we report on the development, implementation and application of a data‐driven, statistical method for mapping SOC stocks in space and time, using Argentina as a pilot. We used quantile regression forest machine learning to predict annual SOC stock at 0–30 cm depth at 250 m resolution for Argentina between 1982 and 2017. The model was calibrated using over 5,000 SOC stock values from the 36‐year time period and 35 environmental covariates. We preprocessed normalized difference vegetation index (NDVI) dynamic covariates using a temporal low‐pass filter to allow the SOC stock for a given year to depend on the NDVI of the current as well as preceding years. Predictions had modest temporal variation, with an average decrease for the entire country from 2.55 to 2.48 kg C m−2 over the 36‐year period (equivalent to a decline of 211 Gg C, 3.0% of the total 0–30 cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62 to 4.34 kg C m−2 (5.9%) during the same period. For the 2001–2015 period, predicted temporal variation was seven‐fold larger than that obtained using the Tier 1 approach of the Intergovernmental Panel on Climate Change and United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and temporal distribution of the calibration data, and the limited explanatory power of the covariates. Cross‐validation confirmed that SOC stock prediction accuracy was limited, with a mean error of 0.03 kg C m−2 and a root mean squared error of 2.04 kg C m−2. In spite of the large uncertainties, this work showed that machine learning methods can be used for space–time SOC mapping and may yield valuable information to land managers and policymakers, provided that SOC observation density in space and time is sufficiently large.Highlights We tested the use of machine learning for space–time mapping of soil organic carbon (SOC) stock. Predictions for Argentina from 1982 to 2017 showed a 3% decrease of the topsoil SOC stock over time. The machine learning model predicted a greater temporal variation than the IPCC Tier 1 approach. Accurate machine learning SOC stock prediction requires dense soil sampling in space and time.