{"type": "FeatureCollection", "features": [{"id": "10.1007/978-3-031-12176-0_11", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:14:07Z", "type": "Report", "created": "2022-11-28", "title": "Integrating X-ray CT Data into Models", "description": "Open AccessXP is a Mar\u00eda Zambrano Fellow at the Public University of Navarra (UPNA) and acknowledges funding from the European Union - NextGenerationEU through the Spanish program 'Ayuda para la Recualificaci\u00f3n del Sistema Universitario Espa\u00f1ol'. AE acknowledges funding from Swiss National Science Foundation: Grants P2EZP2 175128 and P400PB_186751. TR was funded by ERC Consolidator grant 646809 DIMR.", "keywords": ["2. Zero hunger", "570", "550", "X-Ray computed tomography", "[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation", "Soil properties", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "15. 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Given that the main limitation of FAO-2Kc for operational irrigation management over large areas is the unavailability (over most irrigated areas) of irrigation data, this study investigates the feasibility 1) to constrain the FAO-2Kc ET from LST and VI data, 2) to retrieve irrigation amounts and dates from LST and VI data and 3) to estimate the root-zone soil moisture (RZSM) at the daily scale. In practice, the vegetation and soil temperatures retrieved from LST/VI data are used to estimate the FAO-2Kc vegetation stress coefficient (Ks) and soil evaporation reduction coefficient (Kr), respectively. The modeling and remote sensing combined approach is tested over a wheat crop field in central Morocco, and results are evaluated in terms of ET, irrigation and RZSM estimates. ET is estimated with a RMSE of 0.68\u202fmm day-1 compared to 0.84\u202fmm day-1 for the standard (without using LST data) FAO-2Kc based on tabulated values for the parameters. The total irrigation depth (67\u202fmm) is correctly estimated and is very close to the actual effective irrigation (69.8\u202fmm) applied by the farmer. Daily RZSM is estimated with an R2 value of 0.68 (0.42) and a RMSE value of 0.034 (0.061) m3 m-3 by forcing FAO-2Kc using the retrieved irrigation (from LST-derived estimates and precipitation only). Since spaceborne LST data are currently not available at both high-spatial and high-temporal resolution, a sensitivity analysis is finally undertaken to assess the potential and applicability of the proposed methodology to temporally-sparse thermal data.", "keywords": ["FAO-56", "0106 biological sciences", "2. 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Given that the main limitation of FAO-2Kc for operational irrigation management over large areas is the unavailability (over most irrigated areas) of irrigation data, this study investigates the feasibility 1) to constrain the FAO-2Kc ET from LST and VI data, 2) to retrieve irrigation amounts and dates from LST and VI data and 3) to estimate the root-zone soil moisture (RZSM) at the daily scale. In practice, the vegetation and soil temperatures retrieved from LST/VI data are used to estimate the FAO-2Kc vegetation stress coefficient (Ks) and soil evaporation reduction coefficient (Kr), respectively. The modeling and remote sensing combined approach is tested over a wheat crop field in central Morocco, and results are evaluated in terms of ET, irrigation and RZSM estimates. ET is estimated with a RMSE of 0.68\u202fmm day-1 compared to 0.84\u202fmm day-1 for the standard (without using LST data) FAO-2Kc based on tabulated values for the parameters. The total irrigation depth (67\u202fmm) is correctly estimated and is very close to the actual effective irrigation (69.8\u202fmm) applied by the farmer. Daily RZSM is estimated with an R2 value of 0.68 (0.42) and a RMSE value of 0.034 (0.061) m3 m-3 by forcing FAO-2Kc using the retrieved irrigation (from LST-derived estimates and precipitation only). Since spaceborne LST data are currently not available at both high-spatial and high-temporal resolution, a sensitivity analysis is finally undertaken to assess the potential and applicability of the proposed methodology to temporally-sparse thermal data.", "keywords": ["FAO-56", "0106 biological sciences", "2. 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Clean water", "Surface temperature", "[SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation", "[SDE.IE] Environmental Sciences/Environmental Engineering", "Irrigation", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/2809041101"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Water%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2809041101", "name": "item", "description": "2809041101", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2809041101"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-09-01T00:00:00Z"}}, {"id": "10.24072/pcjournal.11", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:20:21Z", "type": "Journal Article", "created": "2020-11-10", "title": "Modelling the impact of the macroalgae Asparagopsis taxiformis on rumen microbial fermentation and methane production", "description": "AbstractBackground<p>The red macroalgae Asparagopsis taxiformis is a potent natural supplement for reducing methane production from cattle. A. taxiformis contains several anti-methanogenic compounds including bromoform that inhibits directly methanogenesis. The positive and adverse effects of A. taxiformis on the rumen microbiota are dose-dependent and operate in a dynamic fashion. It is therefore key to characterize the dynamic response of the rumen microbial fermentation for identifying optimal conditions on the use of A. taxiformis as a dietary supplement for methane mitigation. Accordingly, the objective of this work was to model the effect of A. taxiformis supplementation on the rumen microbial fermentation under in vitro conditions. We adapted a published mathematical model of rumen microbial fermentation to account for A. taxiformis supplementation. We modelled the impact of A. taxiformis on the fermentation and methane production by two mechanisms, namely (i) direct inhibition of the growth rate of methanogens by bromoform and (ii) hydrogen control on sugars utilization and on the flux allocation towards volatile fatty acids production. We calibrated our model using a multi-experiment estimation approach that integrated experimental data with six macroalgae supplementation levels from a published in vitro study assessing the dose-response impact of A. taxiformis on rumen fermentation.</p>Results<p>our model captured satisfactorily the effect of A. taxiformis on the dynamic profile of rumen microbial fermentation for the six supplementation levels of A. taxiformis with an average determination coefficient of 0.88 and an average coefficient of variation of the root mean squared error of 15.2% for acetate, butyrate, propionate, ammonia and methane.</p>Conclusions<p>our results indicated the potential of our model as prediction tool for assessing the impact of additives such as seaweeds on the rumen microbial fermentation and methane production in vitro. Additional dynamic data on hydrogen and bromoform are required to validate our model structure and look for model structure improvements. We expect this model development can be useful to help the design of sustainable nutritional strategies promoting healthy rumen function and low environmental footprint.</p>", "keywords": ["570", "Asparagopsis taxiformis", "[SDV.BIO]Life Sciences [q-bio]/Biotechnology", "methane inhibitors", "Science", "rumen model", "630", "[SDV.EE.ECO]Life Sciences [q-bio]/Ecology", " environment/Ecosystems", "[INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering", "rumen microbiota", "[SDV.EE.ECO] Life Sciences [q-bio]/Ecology", " environment/Ecosystems", "greenhouse gas mitigation", " hydrogen control", " methane inhibitors", " methane mitigation", " red seaweed", " rumen fermentation", " rumen microbiota", " rumen model.", "[SDV.SA.SPA] Life Sciences [q-bio]/Agricultural sciences/Animal production studies", "2. Zero hunger", "methane mitigation", "Q", "greenhouse gas mitigation", "0402 animal and dairy science", "04 agricultural and veterinary sciences", "15. Life on land", "[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation", "[SDV.BIO] Life Sciences [q-bio]/Biotechnology", "rumen fermentation", "Archaeology", "hydrogen control", "13. Climate action", "[SDV.SA.SPA]Life Sciences [q-bio]/Agricultural sciences/Animal production studies", "[SDV.EE.ECO]Life Sciences [q-bio]/Ecology", "red seaweed", "[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation", "environment/Ecosystems", "[INFO.INFO-AU] Computer Science [cs]/Automatic Control Engineering", "CC1-960"]}, "links": [{"href": "https://peercommunityjournal.org/item/10.24072/pcjournal.11.pdf"}, {"href": "https://doi.org/10.24072/pcjournal.11"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Peer%20Community%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.24072/pcjournal.11", "name": "item", "description": "10.24072/pcjournal.11", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.24072/pcjournal.11"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-09T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=%5BINFO.INFO-MO%5DComputer+Science+%5Bcs%5D%2FModeling+and+Simulation&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=%5BINFO.INFO-MO%5DComputer+Science+%5Bcs%5D%2FModeling+and+Simulation&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=%5BINFO.INFO-MO%5DComputer+Science+%5Bcs%5D%2FModeling+and+Simulation&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=%5BINFO.INFO-MO%5DComputer+Science+%5Bcs%5D%2FModeling+and+Simulation&offset=5", "hreflang": "en-US"}], "numberMatched": 5, "numberReturned": 5, "distributedFeatures": [], "timeStamp": "2026-07-27T16:03:59.512810Z"}