{"type": "FeatureCollection", "features": [{"id": "06ad1025-911c-4ffb-82de-cda640fbb4fb", "type": "Feature", "geometry": null, "properties": {"updated": "2025-07-10T11:24:00.699488Z", "type": "Dataset", "language": "it", "title": "MODELLO WRF-ARW a 3km - Temperatura del suolo (C) - (2025-07-10 ore 00 UTC).", "description": "Temperatura del suolo (C). Corsa del 2025-07-10 ore 00 UTC - Valido dalle ore 00 UTC del 2025-07-10 alle ore 00 UTC del 2025-07-13. 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Corsa del 2025-07-10 ore 12 UTC - Valido dalle ore 12 UTC del 2025-07-10 alle ore 00 UTC del 2025-07-14. Modello meteorologico WRF (Weather Research and Forecasting model), core ARW (versione 3.2) con risoluzione spaziale a 3km, risoluzione temporale 60 ore, intervallo 1 ora.", "formats": [{"name": "PNG"}], "keywords": ["120000", "2025-07-10", "20250710t120000000z", "3km", "arw", "below", "between", "content", "depths", "it", "lamma", "layer", "moisture", "soil", "surface", "two", "volumetric"], "contacts": [{"organization": "regione-toscana", "roles": ["creator"]}]}, "links": [{"href": "http://geoportale.lamma.rete.toscana.it/geoserver/ARW_3KM_RUN12/ows"}, {"href": "http://www.lamma.rete.toscana.it/"}, {"href": "https://dati.toscana.it/dataset/modello-wrf-arw-a-3km-umidita-del-suolo-volumetrica-2025-07-10-ore-12-utc#"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250710T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250710T120000000Z_150_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250710T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250710T120000000Z_25_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250710T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250710T120000000Z_5_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250710T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250710T120000000Z_70_0.zip"}, {"href": "http://data.europa.eu/88u/dataset/0ea79f25-efe5-4ad4-aaff-d707b059faf4"}, {"rel": "self", "type": "application/geo+json", "title": "0ea79f25-efe5-4ad4-aaff-d707b059faf4", "name": "item", "description": "0ea79f25-efe5-4ad4-aaff-d707b059faf4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/0ea79f25-efe5-4ad4-aaff-d707b059faf4"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "10.1038/s42949-024-00154-z", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:17:43Z", "type": "Journal Article", "created": "2024-03-16", "title": "Urban greenspaces and nearby natural areas support similar levels of soil ecosystem services", "description": "Abstract<p>Greenspaces are important for sustaining healthy urban environments and their human populations. Yet their capacity to support multiple ecosystem services simultaneously (multiservices) compared with nearby natural ecosystems remains virtually unknown. We conducted a global field survey in 56 urban areas to investigate the influence of urban greenspaces on 23 soil and plant attributes and compared them with nearby natural environments. We show that, in general, urban greenspaces and nearby natural areas support similar levels of soil multiservices, with only six of 23 attributes (available phosphorus, water holding capacity, water respiration, plant cover, arbuscular mycorrhizal fungi (AMF), and arachnid richness) significantly greater in greenspaces, and one (available ammonium) greater in natural areas. Further analyses showed that, although natural areas and urban greenspaces delivered a similar number of services at low (&gt;25% threshold) and moderate (&gt;50%) levels of functioning, natural systems supported significantly more functions at high (&gt;75%) levels of functioning. Management practices (mowing) played an important role in explaining urban ecosystem services, but there were no effects of fertilisation or irrigation. Some services declined with increasing site size, for both greenspaces and natural areas. Our work highlights the fact that urban greenspaces are more similar to natural environments than previously reported and underscores the importance of managing urban greenspaces not only for their social and recreational values, but for supporting multiple ecosystem services on which soils and human well-being depends.</p", "keywords": ["Medio ambiente natural", "2410.05 Ecolog\u00eda Humana", "Health", " Toxicology and Mutagenesis", "0211 other engineering and technologies", "710", "Urban Green Space", "02 engineering and technology", "01 natural sciences", "zelene povr\u0161ine", "ekosistemske storitve", " zelene povr\u0161ine", " urbani gozdovi", " tla", "Urban planning", "Natural (archaeology)", "11. Sustainability", "Urban Heat Islands and Mitigation Strategies", "info:eu-repo/classification/udc/630*1:630*9", "2. Zero hunger", "Global and Planetary Change", "Global Analysis of Ecosystem Services and Land Use", "Geography", "Ecology", "2417.13 Ecolog\u00eda Vegetal", "Carbon cycle", "3. Good health", "soil", " ecosystem services", " urban forests", "2511 Ciencias del Suelo (Edafolog\u00eda)", "Archaeology", "Physical Sciences", "urban forests", "HT361-384", "Ecolog\u00eda (Biolog\u00eda)", "Urbanization. City and country", "Environmental Engineering", "711.4:911.375", "631.4", "Environmental science", "soil", "12. Responsible consumption", "Impact of Urban Green Space on Public Health", "Urban ecosystem", "XXXXXX - Unknown", "Ecosystem services", "14. Life underwater", "Agroforestry", "info:eu-repo/classification/udc/630*1", "Biology", "City planning", "Ecosystem", "0105 earth and related environmental sciences", "SDG-15: Life on land", "tla", "FOS: Environmental engineering", "15. Life on land", "ekosistemske storitve", "Urban ecology", "HT165.5-169.9", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "urbani gozdovi", "502.3", "ecosystem services"]}, "links": [{"href": "https://www.nature.com/articles/s42949-024-00154-z.pdf"}, {"href": "https://doi.org/10.1038/s42949-024-00154-z"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/npj%20Urban%20Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s42949-024-00154-z", "name": "item", "description": "10.1038/s42949-024-00154-z", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s42949-024-00154-z"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-16T00:00:00Z"}}, {"id": "10.1051/agro/2009039", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:17:52Z", "type": "Journal Article", "created": "2010-02-10", "title": "Biofuels, Greenhouse Gases And Climate Change. A Review", "description": "Biofuels are fuels produced from biomass, mostly in liquid form, within a time frame sufficiently short to consider that their feedstock (biomass) can be renewed, contrarily to fossil fuels. This paper reviews the current and future biofuel technologies, and their development impacts (including on the climate) within given policy and economic frameworks. Current technologies make it possible to provide first generation biodiesel, ethanol or biogas to the transport sector to be blended with fossil fuels. Still under-development 2nd generation biofuels from lignocellulose should be available on the market by 2020. Research is active on the improvement of their conversion efficiency. A ten-fold increase compared with current cost-effective capacities would make them highly competitive. Within bioenergy policies, emphasis has been put on biofuels for transportation as this sector is fast-growing and represents a major source of anthropogenic greenhouse gas emissions. Compared with fossil fuels, biofuel combustion can emit less greenhouse gases throughout their life cycle, considering that part of the emitted CO2 returns to the atmosphere where it was fixed from by photosynthesis in the first place. Life cycle assessment (LCA) is commonly used to assess the potential environmental impacts of biofuel chains, notably the impact on global warming. This tool, whose holistic nature is fundamental to avoid pollution trade-offs, is a standardised methodology that should make comparisons between biofuel and fossil fuel chains objective and thorough. However, it is a complex and time-consuming process, which requires lots of data, and whose methodology is still lacking harmonisation. Hence the life-cycle performances of biofuel chains vary widely in the literature. Furthermore, LCA is a site- and timeindependent tool that cannot take into account the spatial and temporal dimensions of emissions, and can hardly serve as a decision-making tool either at local or regional levels. Focusing on greenhouse gases, emission factors used in LCAs give a rough estimate of the potential average emissions on a national level. However, they do not take into account the types of crop, soil or management practices, for instance. Modelling the impact of local factors on the determinism of greenhouse gas emissions can provide better estimates for LCA on the local level, which would be the relevant scale and degree of reliability for decision-making purposes. Nevertheless, a deeper understanding of the processes involved, most notably N2O emissions, is still needed to definitely improve the accuracy of LCA. Perennial crops are a promising option for biofuels, due to their rapid and efficient use of nitrogen, and their limited farming operations. However, the main overall limiting factor to biofuel development will ultimately be land availability. Given the available land areas, population growth rate and consumption behaviours, it would be possible to reach by 2030 a global 10% biofuel share in the transport sector, contributing to lower global greenhouse gas emissions by up to 1 GtCO2 eq.year\u22121 (IEA, 2006), provided that harmonised policies ensure that sustainability criteria for the production systems are respected worldwide. Furthermore, policies should also be more integrative across sectors, so that changes in energy efficiency, the automotive sector and global consumption patterns converge towards drastic reduction of the pressure on resources. Indeed, neither biofuels nor other energy source or carriers are likely to mitigate the impacts of anthropogenic pressure on resources in a range that would compensate for this pressure growth. Hence, the first step is to reduce this pressure by starting from the variable that drives it up, i.e. anthropic consumptions.", "keywords": ["[SDV.SA]Life Sciences [q-bio]/Agricultural sciences", "AGRICULTURAL PRATICES", "P05 - Ressources \u00e9nerg\u00e9tiques et leur gestion", "P06 - Sources d'\u00e9nergie renouvelable", "NITROUS OXIDE", "[SDV]Life Sciences [q-bio]", "CLIMATE CHANGE", "BIOFUELS", "710", "02 engineering and technology", "http://aims.fao.org/aos/agrovoc/c_16181", "7. Clean energy", "http://aims.fao.org/aos/agrovoc/c_2570", "land-use change", "CARBON DIOXIDE", "11. Sustainability", "0202 electrical engineering", " electronic engineering", " information engineering", "gaz \u00e0 effet de serre", "http://aims.fao.org/aos/agrovoc/c_34841", "http://aims.fao.org/aos/agrovoc/c_2018", "\u00e9nergie renouvelable", "POLITICAL AND ECONOMIC FRAMEWORKS", "2. Zero hunger", "changement climatique", "[SDV.SA] Life Sciences [q-bio]/Agricultural sciences", "http://aims.fao.org/aos/agrovoc/c_27465", "bioenergy potential", "nitrous oxide", "LCA", "BIOENERGY POTENTIAL", "LAND-USE CHANGE", "[SDV] Life Sciences [q-bio]", "[SDV.EE] Life Sciences [q-bio]/Ecology", " environment", "source d'\u00e9nergie", "http://aims.fao.org/aos/agrovoc/c_926", "climate change", "politique \u00e9nerg\u00e9tique", "perennials", "ENERGY CROPS", "GREENHOUSE GASES", "http://aims.fao.org/aos/agrovoc/c_28744", "oxyde d'azote", "P40 - M\u00e9t\u00e9orologie et climatologie", "PERENNIALS", "agricultural practices", "pollution par l'agriculture", "12. Responsible consumption", "dioxyde de carbone", "greenhouse gases", "http://aims.fao.org/aos/agrovoc/c_25719", "biomasse", "http://aims.fao.org/aos/agrovoc/c_1302", "http://aims.fao.org/aos/agrovoc/c_1666", "AGRONOMIE", "political and economic frameworks", "energy crops", "pratique culturale", "bio\u00e9nergie", "660", "carbon dioxide", "biofuels", "biocarburant", "http://aims.fao.org/aos/agrovoc/c_16002", "13. Climate action", "http://aims.fao.org/aos/agrovoc/c_16526"]}, "links": [{"href": "https://hal.science/cirad-00749753/file/Article_ASD.2010.pdf"}, {"href": "https://doi.org/10.1051/agro/2009039"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy%20for%20Sustainable%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1051/agro/2009039", "name": "item", "description": "10.1051/agro/2009039", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1051/agro/2009039"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2011-01-01T00:00:00Z"}}, {"id": "10.1088/1748-9326/8/1/015029", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:18:14Z", "type": "Journal Article", "created": "2013-03-07", "title": "Selection Of Appropriate Calculators For Landscape-Scale Greenhouse Gas Assessment For Agriculture And Forestry", "description": "This letter is intended to help potential users select the most appropriate calculator for a landscape-scale greenhouse gas (GHG) assessment of activities for agriculture and forestry. Eighteen calculators were assessed. These calculators were designed for different aims and to be used in different geographical areas and they use slightly different accounting methodologies. The classification proposed is based on the main aim of the assessment: raising awareness, reporting, project evaluation or product assessment. When the aims have been clearly formulated, the most suitable calculator can be selected from the comparison tables, taking account of the geographical area and the scope of the calculation as well as the time and skills required for the calculation. The main issues for interpreting GHG assessments are discussed, highlighting the difficulty of comparing the results obtained from different calculators, mainly owing to differences in scope, calculation methods and reporting units. A major problem is the poor accounting for land use change; the calculators are usually able to account satisfactorily for other emission sources. One of the main challenges at landscape-scale level is to produce a realistic assessment of the various production systems as the uncertainty levels are very high. The results should always give some indication of the link between GHG emissions and the productivity of the area, although no single indicator is able to encompass all the services produced by agriculture and forestry (e.g. food, goods, landscape value and revenue).", "keywords": ["550", "[SDV]Life Sciences [q-bio]", "Science", "QC1-999", "indicateur environnemental", "calculators", "710", "AFOLU", "Environmental technology. Sanitary engineering", "01 natural sciences", "630", "12. Responsible consumption", "mitigation", "greenhouse gases", "11. Sustainability", "gaz \u00e0 effet de serre", "GE1-350", "paysage", "climate", "TD1-1066", "agriculture", "0105 earth and related environmental sciences", "changement climatique", "Physics", "Q", "landscape;carbon calculators;greenhouse gases;GHG emissions;AFOLU;mitigation", "04 agricultural and veterinary sciences", "landscape", "15. Life on land", "carbon calculators", "[SDV] Life Sciences [q-bio]", "GHG emissions", "Environmental sciences", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "https://hal.science/hal-01190664/file/Colomb-EnvResLett-2013_%7B85094A8F-159E-4C0A-9FB9-2DA75BDB27B8%7D.pdf"}, {"href": "https://doi.org/10.1088/1748-9326/8/1/015029"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Research%20Letters", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1088/1748-9326/8/1/015029", "name": "item", "description": "10.1088/1748-9326/8/1/015029", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1088/1748-9326/8/1/015029"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-03-01T00:00:00Z"}}, {"id": "10.1111/gcb.12160", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:18:36Z", "type": "Journal Article", "created": "2013-02-06", "title": "How Much Land-Based Greenhouse Gas Mitigation Can Be Achieved Without Compromising Food Security And Environmental Goals?", "description": "Abstract<p>Feeding 9\uffe2\uff80\uff9310\uffc2\uffa0billion people by 2050 and preventing dangerous climate change are two of the greatest challenges facing humanity. Both challenges must be met while reducing the impact of land management on ecosystem services that deliver vital goods and services, and support human health and well\uffe2\uff80\uff90being. Few studies to date have considered the interactions between these challenges. In this study we briefly outline the challenges, review the supply\uffe2\uff80\uff90 and demand\uffe2\uff80\uff90side climate mitigation potential available in the Agriculture, Forestry and Other Land Use AFOLU sector and options for delivering food security. We briefly outline some of the synergies and trade\uffe2\uff80\uff90offs afforded by mitigation practices, before presenting an assessment of the mitigation potential possible in theAFOLUsector under possible future scenarios in which demand\uffe2\uff80\uff90side measures codeliver to aid food security. We conclude that while supply\uffe2\uff80\uff90side mitigation measures, such as changes in land management, might either enhance or negatively impact food security, demand\uffe2\uff80\uff90side mitigation measures, such as reduced waste or demand for livestock products, should benefit both food security and greenhouse gas (GHG) mitigation. Demand\uffe2\uff80\uff90side measures offer a greater potential (1.5\uffe2\uff80\uff9315.6\uffc2\uffa0GtCO2\uffe2\uff80\uff90eq. yr\uffe2\uff88\uff921) in meeting both challenges than do supply\uffe2\uff80\uff90side measures (1.5\uffe2\uff80\uff934.3\uffc2\uffa0GtCO2\uffe2\uff80\uff90eq. yr\uffe2\uff88\uff921at carbon prices between 20 and 100\uffc2\uffa0US$ tCO2\uffe2\uff80\uff90eq. yr\uffe2\uff88\uff921), but given the enormity of challenges, all options need to be considered. Supply\uffe2\uff80\uff90side measures should be implemented immediately, focussing on those that allow the production of more agricultural product per unit of input. For demand\uffe2\uff80\uff90side measures, given the difficulties in their implementation and lag in their effectiveness, policy should be introduced quickly, and should aim to codeliver to other policy agenda, such as improving environmental quality or improving dietary health. These problems facing humanity in the 21st Century are extremely challenging, and policy that addresses multiple objectives is required now more than ever.</p>", "keywords": ["Greenhouse Effect", "Conservation of Natural Resources", "Mitigation", "330", "Climate", "Climate Change", "AFOLU", "710", "01 natural sciences", "7. Clean energy", "630", "Food Supply", "12. Responsible consumption", "11. Sustainability", "Ecosystem services", "Humans", "Ecosystem", "0105 earth and related environmental sciences", "2. Zero hunger", "Agriculture", "Forestry", "food security", "Food security", "15. Life on land", "6. Clean water", "004", "13. Climate action", "GHG", "Gases", "environment"]}, "links": [{"href": "https://doi.org/10.1111/gcb.12160"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/gcb.12160", "name": "item", "description": "10.1111/gcb.12160", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcb.12160"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-05-29T00:00:00Z"}}, {"id": "10.1146/annurev-environ-101718-033129", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:19:14Z", "type": "Journal Article", "created": "2019-06-11", "title": "Land-Management Options for Greenhouse Gas Removal and Their Impacts on Ecosystem Services and the Sustainable Development Goals", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p> Land-management options for greenhouse gas removal (GGR) include afforestation or reforestation (AR), wetland restoration, soil carbon sequestration (SCS), biochar, terrestrial enhanced weathering (TEW), and bioenergy with carbon capture and storage (BECCS). We assess the opportunities and risks associated with these options through the lens of their potential impacts on ecosystem services (Nature's Contributions to People; NCPs) and the United Nations Sustainable Development Goals (SDGs). We find that all land-based GGR options contribute positively to at least some NCPs and SDGs. Wetland restoration and SCS almost exclusively deliver positive impacts. A few GGR options, such as afforestation, BECCS, and biochar potentially impact negatively some NCPs and SDGs, particularly when implemented at scale, largely through competition for land. For those that present risks or are least understood, more research is required, and demonstration projects need to proceed with caution. For options that present low risks and provide cobenefits, implementation can proceed more rapidly following no-regrets principles. </p></article>", "keywords": ["330", "Sustainable Development Goals", "710", "SDG", "CDR", "01 natural sciences", "333", "nature's contributions to people", "12. Responsible consumption", "wetland restoration", "soil carbon sequestration", "negative emission technology", "afforestation/reforestation", "11. Sustainability", "BECCS", "NCPs", "biochar", "UN Sustainable Development Goals", "carbon dioxide removal", "0105 earth and related environmental sciences", "2. Zero hunger", "bioenergy with carbon capture and storage", "greenhouse gas removal", "15. Life on land", "6. Clean water", "SDG 15", "NET", "Nature's Contributions to People", "13. Climate action", "ecosystem services", "terrestrial enhanced weathering"]}, "links": [{"href": "https://www.annualreviews.org/doi/pdf/10.1146/annurev-environ-101718-033129"}, {"href": "https://doi.org/10.1146/annurev-environ-101718-033129"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Annual%20Review%20of%20Environment%20and%20Resources", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1146/annurev-environ-101718-033129", "name": "item", "description": "10.1146/annurev-environ-101718-033129", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1146/annurev-environ-101718-033129"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-10-17T00:00:00Z"}}, {"id": "10.3390/rs14092256", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:03Z", "type": "Journal Article", "created": "2022-05-09", "title": "Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Agriculture is the backbone and the main sector of the industry for many countries in the world. Assessing crop yields is key to optimising on-field decisions and defining sustainable agricultural strategies. Remote sensing applications have greatly enhanced our ability to monitor and manage farming operation. The main objective of this research was to evaluate machine learning system for within-field soya yield prediction trained on Sentinel-2 multispectral images and soil parameters. Multispectral images used in the study came from ESA\u2019s Sentinel-2 satellites. A total of 3 cloud-free Sentinel-2 multispectral images per year from specific periods of vegetation were used to obtain the time-series necessary for crop yield prediction. Yield monitor data were collected in three crop seasons (2018, 2019 and 2020) from a number of farms located in Upper Austria. The ground-truth database consisted of information about the location of the fields and crop yield monitor data on 411 ha of farmland. A novel method, namely the Polygon-Pixel Interpolation, for optimal fitting yield monitor data with satellite images is introduced. Several machine learning algorithms, such as Multiple Linear Regression, Support Vector Machine, eXtreme Gradient Boosting, Stochastic Gradient Descent and Random Forest, were compared for their performance in soya yield prediction. Among the tested machine learning algorithms, Stochastic Gradient Descent regression model performed better than the others, with a mean absolute error of 4.36 kg/pixel (0.436 t/ha) and a correlation coefficient of 0.83%.</p></article>", "keywords": ["2. Zero hunger", "precision agriculture", "stochastic gradient descent (SGD)", "polygon-pixel intersection (PPI)", "Science", "Q", "710", "high performance computing (HPC)", "04 agricultural and veterinary sciences", "15. Life on land", "630", "620", "remote sensing", "precision agriculture; remote sensing; polygon-pixel intersection (PPI); stochastic gradient descent (SGD); high performance computing (HPC)", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/9/2256/pdf"}, {"href": "https://doi.org/10.3390/rs14092256"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs14092256", "name": "item", "description": "10.3390/rs14092256", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14092256"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-07T00:00:00Z"}}, {"id": "10.18419/opus-12581", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:19:50Z", "type": "Journal Article", "created": "2022-05-08", "title": "Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Agriculture is the backbone and the main sector of the industry for many countries in the world. Assessing crop yields is key to optimising on-field decisions and defining sustainable agricultural strategies. Remote sensing applications have greatly enhanced our ability to monitor and manage farming operation. The main objective of this research was to evaluate machine learning system for within-field soya yield prediction trained on Sentinel-2 multispectral images and soil parameters. Multispectral images used in the study came from ESA\u2019s Sentinel-2 satellites. A total of 3 cloud-free Sentinel-2 multispectral images per year from specific periods of vegetation were used to obtain the time-series necessary for crop yield prediction. Yield monitor data were collected in three crop seasons (2018, 2019 and 2020) from a number of farms located in Upper Austria. The ground-truth database consisted of information about the location of the fields and crop yield monitor data on 411 ha of farmland. A novel method, namely the Polygon-Pixel Interpolation, for optimal fitting yield monitor data with satellite images is introduced. Several machine learning algorithms, such as Multiple Linear Regression, Support Vector Machine, eXtreme Gradient Boosting, Stochastic Gradient Descent and Random Forest, were compared for their performance in soya yield prediction. Among the tested machine learning algorithms, Stochastic Gradient Descent regression model performed better than the others, with a mean absolute error of 4.36 kg/pixel (0.436 t/ha) and a correlation coefficient of 0.83%.</p></article>", "keywords": ["2. Zero hunger", "precision agriculture", "stochastic gradient descent (SGD)", "polygon-pixel intersection (PPI)", "Science", "Q", "710", "high performance computing (HPC)", "04 agricultural and veterinary sciences", "15. Life on land", "630", "620", "remote sensing", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/9/2256/pdf"}, {"href": "https://doi.org/10.18419/opus-12581"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.18419/opus-12581", "name": "item", "description": "10.18419/opus-12581", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.18419/opus-12581"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-07T00:00:00Z"}}, {"id": "10.3390/w15040694", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:21:07Z", "type": "Journal Article", "created": "2023-02-10", "title": "Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Agriculture has significantly aided in meeting the food needs of growing population. In addition, it has boosted economic development in irrigated regions. In this study, an assessment of the groundwater (GW) quality for agricultural land was carried out in El Kharga Oasis, Western Desert of Egypt. Several irrigation water quality indices (IWQIs) and geographic information systems (GIS) were used for the modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)) were developed for the prediction of eight IWQIs, including the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), potential salinity (PS), residual sodium carbonate index (RSC), and Kelley index (KI). The physicochemical parameters included T\u00b0, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl\u2212, SO42\u2212, HCO3\u2212, CO32\u2212, and NO3\u2212, and they were measured in 140 GW wells. The hydrochemical facies of the GW resources were of Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 types, which revealed silicate weathering, dissolution of gypsum/calcite/dolomite/ halite, rock\u2013water interactions, and reverse ion exchange processes. The IWQI, SAR, KI, and PS showed that the majority of the GW samples were categorized for irrigation purposes into no restriction (67.85%), excellent (100%), good (57.85%), and excellent to good (65.71%), respectively. Moreover, the majority of the selected samples were categorized as excellent to good and safe for irrigation according to the SSP and RSC. The performance of the simulation models was evaluated based on several prediction skills criteria, which revealed that the ANFIS model and SVM model were capable of simulating the IWQIs with reasonable accuracy for both training \u201cdetermination coefficient (R2)\u201d (R2 = 0.99 and 0.97) and testing (R2 = 0.97 and 0.76). The presented models\u2019 promising accuracy illustrates their potential for use in IWQI prediction. The findings indicate the potential for ML methods of geographically dispersed hydrogeochemical data, such as ANFIS and SVM, to be used for assessing the GW quality for irrigation. The proposed methodological approach offers a useful tool for identifying the crucial hydrogeochemical components for GW evolution assessment and mitigation measures related to GW management in arid and semi-arid environments.</p></article>", "keywords": ["2. Zero hunger", "machine learning", "groundwater quality", "hydrogeochemistry", "water quality indices", "710", "14. Life underwater", "15. Life on land", "01 natural sciences", "irrigation", "6. Clean water", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/15/4/694/pdf"}, {"href": "https://www.mdpi.com/2073-4441/15/4/694/pdf"}, {"href": "https://research.usq.edu.au/download/1c0f24478d75e81d1b30c7d2ef129cd978901a29587ebd125c32afb1fbbe09b0/16662935/Evaluation%20and%20Prediction%20of%20Groundwater%20Quality.pdf"}, {"href": "https://doi.org/10.3390/w15040694"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/w15040694", "name": "item", "description": "10.3390/w15040694", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/w15040694"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-10T00:00:00Z"}}, {"id": "10.5281/zenodo.15827808", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:23:01Z", "type": "Journal Article", "created": "2023-02-10", "title": "Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Agriculture has significantly aided in meeting the food needs of growing population. In addition, it has boosted economic development in irrigated regions. In this study, an assessment of the groundwater (GW) quality for agricultural land was carried out in El Kharga Oasis, Western Desert of Egypt. Several irrigation water quality indices (IWQIs) and geographic information systems (GIS) were used for the modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)) were developed for the prediction of eight IWQIs, including the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), potential salinity (PS), residual sodium carbonate index (RSC), and Kelley index (KI). The physicochemical parameters included T\u00b0, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl\u2212, SO42\u2212, HCO3\u2212, CO32\u2212, and NO3\u2212, and they were measured in 140 GW wells. The hydrochemical facies of the GW resources were of Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 types, which revealed silicate weathering, dissolution of gypsum/calcite/dolomite/ halite, rock\u2013water interactions, and reverse ion exchange processes. The IWQI, SAR, KI, and PS showed that the majority of the GW samples were categorized for irrigation purposes into no restriction (67.85%), excellent (100%), good (57.85%), and excellent to good (65.71%), respectively. Moreover, the majority of the selected samples were categorized as excellent to good and safe for irrigation according to the SSP and RSC. The performance of the simulation models was evaluated based on several prediction skills criteria, which revealed that the ANFIS model and SVM model were capable of simulating the IWQIs with reasonable accuracy for both training \u201cdetermination coefficient (R2)\u201d (R2 = 0.99 and 0.97) and testing (R2 = 0.97 and 0.76). The presented models\u2019 promising accuracy illustrates their potential for use in IWQI prediction. The findings indicate the potential for ML methods of geographically dispersed hydrogeochemical data, such as ANFIS and SVM, to be used for assessing the GW quality for irrigation. The proposed methodological approach offers a useful tool for identifying the crucial hydrogeochemical components for GW evolution assessment and mitigation measures related to GW management in arid and semi-arid environments.</p></article>", "keywords": ["2. Zero hunger", "machine learning", "groundwater quality", "hydrogeochemistry", "water quality indices", "710", "14. Life underwater", "15. Life on land", "01 natural sciences", "irrigation", "6. Clean water", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/15/4/694/pdf"}, {"href": "https://www.mdpi.com/2073-4441/15/4/694/pdf"}, {"href": "https://research.usq.edu.au/download/1c0f24478d75e81d1b30c7d2ef129cd978901a29587ebd125c32afb1fbbe09b0/16662935/Evaluation%20and%20Prediction%20of%20Groundwater%20Quality.pdf"}, {"href": "https://doi.org/10.5281/zenodo.15827808"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15827808", "name": "item", "description": "10.5281/zenodo.15827808", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15827808"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-10T00:00:00Z"}}, {"id": "20.500.11850/548479", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:14Z", "type": "Journal Article", "title": "A well-established fact: Rapid mineralization of organic inputs is an important factor for soil carbon sequestration", "description": "Open AccessISSN:1365-2389", "keywords": ["P33 - Chimie et physique du sol", "2. Zero hunger", "http://aims.fao.org/aos/agrovoc/c_1374571087594", "P40 - M\u00e9t\u00e9orologie et climatologie", "P34 - Biologie du sol", "04 agricultural and veterinary sciences", "15. Life on land", "min\u00e9ralisation du carbone", "http://aims.fao.org/aos/agrovoc/c_331583", "carbon sequestration", "soil", "sciences du sol", "s\u00e9questration du carbone", "http://aims.fao.org/aos/agrovoc/c_36244", "climate change", "carbon sequestration; climate change; mineralization; soil", "13. Climate action", "carbone organique du sol", "0401 agriculture", " forestry", " and fisheries", "http://aims.fao.org/aos/agrovoc/c_389fe908", "mineralization", "min\u00e9ralisation", "http://aims.fao.org/aos/agrovoc/c_15999", "http://aims.fao.org/aos/agrovoc/c_7188", "att\u00e9nuation des effets du changement climatique"], "contacts": [{"organization": "Angers, Denis, Arrouays, Dominique, Cardinael, R\u00e9mi, Chenu, Claire, Corbeels, Marc, Demenois, Julien, Farrell, Mark, Martin, Manuel, Minasny, Budiman, Recous, Sylvie, Six, Johan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/20.500.11850/548479"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/European%20Journal%20of%20Soil%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11850/548479", "name": "item", "description": "20.500.11850/548479", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11850/548479"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "1959.13/1433083", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:05Z", "type": "Journal Article", "created": "2019-06-11", "title": "Land-Management Options for Greenhouse Gas Removal and Their Impacts on Ecosystem Services and the Sustainable Development Goals", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p> Land-management options for greenhouse gas removal (GGR) include afforestation or reforestation (AR), wetland restoration, soil carbon sequestration (SCS), biochar, terrestrial enhanced weathering (TEW), and bioenergy with carbon capture and storage (BECCS). We assess the opportunities and risks associated with these options through the lens of their potential impacts on ecosystem services (Nature's Contributions to People; NCPs) and the United Nations Sustainable Development Goals (SDGs). We find that all land-based GGR options contribute positively to at least some NCPs and SDGs. Wetland restoration and SCS almost exclusively deliver positive impacts. A few GGR options, such as afforestation, BECCS, and biochar potentially impact negatively some NCPs and SDGs, particularly when implemented at scale, largely through competition for land. For those that present risks or are least understood, more research is required, and demonstration projects need to proceed with caution. For options that present low risks and provide cobenefits, implementation can proceed more rapidly following no-regrets principles. </p></article>", "keywords": ["330", "Sustainable Development Goals", "710", "SDG", "CDR", "01 natural sciences", "333", "nature's contributions to people", "12. Responsible consumption", "wetland restoration", "soil carbon sequestration", "negative emission technology", "afforestation/reforestation", "11. Sustainability", "BECCS", "NCPs", "biochar", "UN Sustainable Development Goals", "carbon dioxide removal", "0105 earth and related environmental sciences", "2. Zero hunger", "bioenergy with carbon capture and storage", "greenhouse gas removal", "15. Life on land", "6. Clean water", "SDG 15", "NET", "Nature's Contributions to People", "13. Climate action", "ecosystem services", "terrestrial enhanced weathering"]}, "links": [{"href": "https://www.annualreviews.org/doi/pdf/10.1146/annurev-environ-101718-033129"}, {"href": "https://doi.org/1959.13/1433083"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Annual%20Review%20of%20Environment%20and%20Resources", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1959.13/1433083", "name": "item", "description": "1959.13/1433083", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1959.13/1433083"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-10-17T00:00:00Z"}}, {"id": "11191/5789", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:24:46Z", "type": "Report", "title": "Crisis y respuesta gubernamental en la industria automotriz: M\u00e9xico y el contexto internacional", "description": "Open Access75 p\u00e1ginas. Maestr\u00eda en Econom\u00eda.", "keywords": ["Autom\u00f3viles -- Industria y comercio", "330", "Crisis econ\u00f3mica.", "Tecnolog\u00eda", "Pol\u00edticas; Estrategia de las empresas; Tecnolog\u00eda; Tratado de Libre Comercio de Am\u00e9rica del Norte.", "Pol\u00edticas", "Automobile industry and trade--Mexico", "Autom\u00f3viles -- Industria y comercio.", "Tratado de Libre Comercio de Am\u00e9rica del Norte", "Automobile industry and trade--Mexico.", "CIENCIAS SOCIALES::CIENCIAS ECON\u00d3MICAS::ORGANIZACI\u00d3N INDUSTRIAL Y POL\u00cdTICA P\u00daBLICA::REGULACI\u00d3N GUBERNAMENTAL DEL SECTOR PRIVADO", "HD9710.M42", "Estrategia de las empresas", "Crisis econ\u00f3mica"], "contacts": [{"organization": "MELO NEPONUCENO, EUGENIA, 254386,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/11191/5789"}, {"rel": "self", "type": "application/geo+json", "title": "11191/5789", "name": "item", "description": "11191/5789", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11191/5789"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2010-03-01T00:00:00Z"}}, {"id": "1959.7/uws:76472", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:25:07Z", "type": "Journal Article", "created": "2024-03-16", "title": "Urban greenspaces and nearby natural areas support similar levels of soil ecosystem services", "description": "Abstract<p>Greenspaces are important for sustaining healthy urban environments and their human populations. Yet their capacity to support multiple ecosystem services simultaneously (multiservices) compared with nearby natural ecosystems remains virtually unknown. We conducted a global field survey in 56 urban areas to investigate the influence of urban greenspaces on 23 soil and plant attributes and compared them with nearby natural environments. We show that, in general, urban greenspaces and nearby natural areas support similar levels of soil multiservices, with only six of 23 attributes (available phosphorus, water holding capacity, water respiration, plant cover, arbuscular mycorrhizal fungi (AMF), and arachnid richness) significantly greater in greenspaces, and one (available ammonium) greater in natural areas. Further analyses showed that, although natural areas and urban greenspaces delivered a similar number of services at low (&gt;25% threshold) and moderate (&gt;50%) levels of functioning, natural systems supported significantly more functions at high (&gt;75%) levels of functioning. Management practices (mowing) played an important role in explaining urban ecosystem services, but there were no effects of fertilisation or irrigation. Some services declined with increasing site size, for both greenspaces and natural areas. Our work highlights the fact that urban greenspaces are more similar to natural environments than previously reported and underscores the importance of managing urban greenspaces not only for their social and recreational values, but for supporting multiple ecosystem services on which soils and human well-being depends.</p", "keywords": ["Medio ambiente natural", "2410.05 Ecolog\u00eda Humana", "Health", " Toxicology and Mutagenesis", "0211 other engineering and technologies", "710", "Urban Green Space", "02 engineering and technology", "01 natural sciences", "zelene povr\u0161ine", "Urban planning", "Natural (archaeology)", "11. Sustainability", "Urban Heat Islands and Mitigation Strategies", "info:eu-repo/classification/udc/630*1:630*9", "2. Zero hunger", "Global and Planetary Change", "Global Analysis of Ecosystem Services and Land Use", "Geography", "Ecology", "2417.13 Ecolog\u00eda Vegetal", "Carbon cycle", "3. Good health", "2511 Ciencias del Suelo (Edafolog\u00eda)", "Archaeology", "Physical Sciences", "urban forests", "HT361-384", "Ecolog\u00eda (Biolog\u00eda)", "Urbanization. City and country", "Environmental Engineering", "711.4:911.375", "631.4", "Environmental science", "soil", "12. Responsible consumption", "Impact of Urban Green Space on Public Health", "Urban ecosystem", "XXXXXX - Unknown", "Ecosystem services", "14. Life underwater", "Agroforestry", "Biology", "City planning", "Ecosystem", "0105 earth and related environmental sciences", "SDG-15: Life on land", "tla", "FOS: Environmental engineering", "15. Life on land", "ekosistemske storitve", "Urban ecology", "HT165.5-169.9", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "urbani gozdovi", "ecosystem services", "502.3"]}, "links": [{"href": "https://www.nature.com/articles/s42949-024-00154-z.pdf"}, {"href": "https://doi.org/1959.7/uws:76472"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/npj%20Urban%20Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1959.7/uws:76472", "name": "item", "description": "1959.7/uws:76472", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1959.7/uws:76472"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-16T00:00:00Z"}}, {"id": "50|od______3631::788b68858ed6ceec284f239e36d1e6eb", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-25T16:26:37Z", "type": "Report", "title": "A marginal abatement cost curve for greenhouse gases attenuation by additional carbon storage in french agricultural land", "description": "Following the Paris agreement in 2015, the European Union (EU) set a carbon neutrality objective by 2050, and so did France. The French agricultural sector can contribute as a carbon sink through carbon storage in biomass and soil, in addition to reducing GHG emissions. The objective of this study is to quantitatively assess the additional storage potential and cost of a set of eight carbon-storing practices. The impacts of these agricultural practices on soil organic carbon storage and crop production are assessed at a very fine spatial scale, using crop and grassland models. The associated area base, GHG budget, and implementation costs are assessed and aggregated at the region level. The economic model BANCO uses this information to derive the marginal abatement cost curve for France and identify the combination of carbon storing practices that minimizes the total cost of achieving a given national net GHG mitigation target. We find that a substantial amount of carbon, 36.2 to 52.9 MtCO2e yr\u22121, can be stored in soil and biomass for reasonable carbon prices of 55 and 250 \u20ac tCO2e\u22121, respectively (corresponding to current and 2030 French carbon value for climate action), mainly by developing agroforestry and hedges, generalising cover crops, and introducing or extending temporary grasslands in crop sequences. This finding questions the 3\u20135 times lower target of 10 MtCO2e.yr\u22121 retained for the agricultural carbon sink by the French climate neutrality strategy. Overall, this would decrease total French GHG emissions by 9.2\u201313.8%, respectively (reference year 2019).", "keywords": ["2. Zero hunger", "P33 - Chimie et physique du sol", "http://aims.fao.org/aos/agrovoc/c_1374571087594", "P40 - M\u00e9t\u00e9orologie et climatologie", "F08 - Syst\u00e8mes et modes de culture", "\u00e9mission de gaz", "terre agricole", "co\u00fbt marginal", "http://aims.fao.org/aos/agrovoc/c_331597", "15. Life on land", "http://aims.fao.org/aos/agrovoc/c_28725", "7. Clean energy", "http://aims.fao.org/aos/agrovoc/c_331583", "http://aims.fao.org/aos/agrovoc/c_0d4560a5", "http://aims.fao.org/aos/agrovoc/c_2808", "s\u00e9questration du carbone", "13. Climate action", "r\u00e9duction des \u00e9missions", "11. 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