{"type": "FeatureCollection", "features": [{"id": "20.500.11850/548479", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:25:29Z", "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. 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For those models aiming at prediction, validation is a critical step to gain confidence in projections. With a comprehensive review of ~250 models, we assess how models are validated depending on their objectives and features, discuss how validation of predictive models can be improved. We find a critical lack of independent validation using observed time series. Conducting such validations should be a priority to improve the model reliability. Approximately 60% of the models we analysed are not designed for predictions, but rather for conceptual understanding of soil processes. These models provide important insights by identifying key processes and alternative formalisms that can be relevant for predictive models. We argue that combining independent validation based on observed time series and improved information flow between predictive and conceptual models will increase reliability in predictions.</p", "keywords": ["P33 - Chimie et physique du sol", "[SDE] Environmental Sciences", "P40 - M\u00e9t\u00e9orologie et climatologie", "analyse de s\u00e9ries chronologiques", "01 natural sciences", "2300 General Environmental Science", "http://aims.fao.org/aos/agrovoc/c_3041", "carbone organique du sol", "http://aims.fao.org/aos/agrovoc/c_389fe908", "GE1-350", "910 Geography & travel", "mod\u00e9lisation", "0105 earth and related environmental sciences", "QE1-996.5", "http://aims.fao.org/aos/agrovoc/c_230ab86c", "U10 - Informatique", " math\u00e9matiques et statistiques", "1900 General Earth and Planetary Sciences", "Geology", "04 agricultural and veterinary sciences", "15. 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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. 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