{"type": "FeatureCollection", "features": [{"id": "10.1016/j.agwat.2021.106774", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:15:21Z", "type": "Journal Article", "created": "2021-02-05", "title": "Numerically scheduling plant water deficit index-based smart irrigation to optimize crop yield and water use efficiency", "description": "Open AccessThis research was supported partly by National Key Research and Development Program of China (2016YFD0200303, 2017YFE0118100), National Natural Science Foundation of China (U1706211, 51790532), Special Fund for Scientific Research in the Public Interest (201411009), and the European Union\u2019s Horizon 2020 Research and Innovation Programme under Project SHui, grant agreement No 773903.", "keywords": ["0106 biological sciences", "2. Zero hunger", "Crop growth", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Soil water transport", "01 natural sciences", "Irrigation scheduling", "Decision support system", "Regulated deficit irrigation", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.1016/j.agwat.2021.106774"}, {"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": "10.1016/j.agwat.2021.106774", "name": "item", "description": "10.1016/j.agwat.2021.106774", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agwat.2021.106774"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-01T00:00:00Z"}}, {"id": "10.1016/j.eja.2022.126569", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:15:45Z", "type": "Journal Article", "created": "2022-07-08", "title": "Mixing process-based and data-driven approaches in yield prediction", "description": "Yield prediction models can be divided between data-driven and process-based models (crop growth models). The first category contains many different types of models with parameters learned from the data themselves and where domain knowledge is only used to select the predictors and engineer features. In the second category, models are based upon biophysical principles, whose structure and parameters are derived primarily from domain knowledge. Here we investigate if the integration of the two approaches can be beneficial as it allows to overcome the limitations of the two approaches taken individually - lack of sufficiently large, reliable and orthogonal datasets for data-driven approaches and the need of many inputs for process-based models. The applications of the two categories of models have been reviewed, paying special attention to the cases where the two approaches have been mixed. By analysing the literature we identified three major cases of integration between the two approaches: (1) using crop growth models to engineer features and expand the predictors space, (2) use data-driven approaches to estimate missing inputs for process-based models (3) using data-driven approaches to produce meta-models to reduce computation burden. Finally we propose a methodology based on metamodels and transfer learning to integrate data-driven and process-based approaches.", "keywords": ["Process-based", "0106 biological sciences", "2. Zero hunger", "Artificial intelligence", "Crop growth models", "04 agricultural and veterinary sciences", "Data-driven", "01 natural sciences", "Yield prediction", "Dynamic crop growth models", "Surrogate models", "0401 agriculture", " forestry", " and fisheries", "Crop models", "Metamodels", "Neural networks"]}, "links": [{"href": "https://doi.org/10.1016/j.eja.2022.126569"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/European%20Journal%20of%20Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.eja.2022.126569", "name": "item", "description": "10.1016/j.eja.2022.126569", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.eja.2022.126569"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-01T00:00:00Z"}}, {"id": "10.1016/j.fcr.2010.08.012", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:15:53Z", "type": "Journal Article", "created": "2010-09-29", "title": "The Effect Of Tillage, Crop Rotation And Residue Management On Maize And Wheat Growth And Development Evaluated With An Optical Sensor", "description": "Abstract   Crop growth and development as well as yield are the result of the efficiency of the chosen agricultural management system within the boundaries of the agro-ecological environment. End-of-season yield results do not permit the evaluation of within-season management interactions with the production environment and do not allow for full understanding of the management practice applied. Crop growth and development were measured during the 2004, 2006 and 2008 crop cycles with an optical handheld NDVI sensor for all plots of the different management treatments of a long-term (since 1991) sustainability trial in the highlands of Mexico. Cropping systems varying in (1) tillage (conventional vs. zero tillage); (2) residue management (retention vs. removal); (3) rotation (monocropping vs. a maize [ Zea mays  L.]/wheat [ Triticum aestivum  L.] rotation) were compared. The NDVI-handheld sensor was evaluated as a tool to monitor crop growth and development and was found to be an excellent tool for this purpose. There was a strong relation between NDVI and biomass accumulation of maize and wheat. The measurement with the handheld sensor was non-destructive and fast so that a representative plot area could be measured easily and time-efficiently. Zero tillage induced different crop growth dynamics over time compared to conventional tillage. Zero tillage with residue retention is characterized by a slower initial crop growth, compensated for by an increased growth in the later stages, positively influencing final grain yield. Also crop rotation influenced early crop growth, with lower NDVI values for crops sown after wheat than crops after maize. Zero tillage with residue removal had low NDVI values throughout the growing season. Zero tillage with retention of crop residues results in time efficient use of resources, as opposed to conventional tillage, regardless of residue management, and zero tillage with residue removal. The results indicated that different tillage, rotation and residue management practices influence crop growth and development. It is important to monitor and understand crop growth under different management systems to select the right varieties and adjust timing and practice of input supply (fertilizer, irrigation etc.) in a holistic way in each cropping system.", "keywords": ["2. Zero hunger", "Conservation agriculture", "Ecoagriculture", "Residue management", "Triticum aestivum", "04 agricultural and veterinary sciences", "15. Life on land", "Crop rotations", "Semiarid zones", "Zero tillage", "Soil conservation", "Crop growth", "Zea mays l.", "0401 agriculture", " forestry", " and fisheries", "Field Scale", "Rainfed agriculture", "Ndvi hand-held sensor"]}, "links": [{"href": "https://doi.org/10.1016/j.fcr.2010.08.012"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Field%20Crops%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.fcr.2010.08.012", "name": "item", "description": "10.1016/j.fcr.2010.08.012", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.fcr.2010.08.012"}, {"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": "10.1046/j.1469-8137.2001.00114.x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:17:45Z", "type": "Journal Article", "created": "2003-03-12", "title": "Do Species And Functional Groups Differ In Acquisition And Use Of C, N And Water Under Varying Atmospheric Co2 And N Availability Regimes? A Field Test With 16 Grassland Species", "description": "Summary<p>  <p>\uffe2\uff80\uff82To evaluate whether functional groups have a similar response to global change, the responses to CO2 concentration and N availability of grassland species from several functional groups are reported here.</p> <p>\uffe2\uff80\uff82Sixteen perennial grassland species from four trait\uffe2\uff80\uff90based functional groups (C3 grasses, C4 grasses, non\uffe2\uff80\uff90leguminous forbs, legumes) were grown in field monocultures under ambient or elevated (560\uffc2\uffa0\uffc2\uffb5mol mol\uffe2\uff88\uff921) CO2 using free\uffe2\uff80\uff90air CO2 enrichment (FACE), in low N (unamended field soil) or high N (field soil +4\uffc2\uffa0g\uffc2\uffa0N\uffc2\uffa0m\uffe2\uff88\uff922 years\uffe2\uff88\uff921) treatments.</p> <p>\uffe2\uff80\uff82There were no CO2\uffc2\uffa0\uffc3\uff97\uffc2\uffa0N interactions. Functional groups responded differently to CO2 and N in terms of biomass, tissue N concentration and soil solution N. Under elevated CO2, forbs, legumes and C3 grasses increased total biomass by 31%, 18%, and 9%, respectively, whereas biomass was reduced in C4\uffe2\uff80\uff90grass monocultures. Two of the four legume species increased biomass and total plant N pools under elevated CO2, probably due to stimulated N\uffe2\uff80\uff90fixation. Only one species markedly shifted the proportional distribution of below\uffe2\uff80\uff90 vs aboveground biomass in response to CO2 or N.</p> <p>\uffe2\uff80\uff82Although functional groups varied in responses to CO2 and N, there was also substantial variation in responses among species within groups. These results suggest that current trait\uffe2\uff80\uff90based functional classifications might be useful, but not sufficient, for understanding plant and ecosystem responses to elevated CO2 and N availability.</p>  </p>", "keywords": ["580", "N availability", "2. Zero hunger", "0106 biological sciences", "570", "biomass", "legumes", "070601 - Horticultural Crop Growth and Development", "elevated carbon dioxide", "carbon dioxide", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "nitrogen", "forbs", "grasses", "Natural Resources and Conservation", "Functional groups", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "https://doi.org/10.1046/j.1469-8137.2001.00114.x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/New%20Phytologist", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1046/j.1469-8137.2001.00114.x", "name": "item", "description": "10.1046/j.1469-8137.2001.00114.x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1046/j.1469-8137.2001.00114.x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2001-05-01T00:00:00Z"}}, {"id": "10.3390/agronomy9050255", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:20:42Z", "type": "Journal Article", "created": "2019-05-21", "title": "Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.</p></article>", "keywords": ["2. Zero hunger", "yield estimation", "S", "Leaf Area Index", "EPIC model", "Agriculture", "Crop growth model", "04 agricultural and veterinary sciences", "15. Life on land", "crop growth model", "Yield estimation", "13. Climate action", "Leaf area index", "Data assimilation", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "data assimilation"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://doi.org/10.3390/agronomy9050255"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agronomy9050255", "name": "item", "description": "10.3390/agronomy9050255", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy9050255"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-21T00:00:00Z"}}, {"id": "10.5194/egusphere-egu2020-21951", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:21:43Z", "type": "Journal Article", "created": "2019-05-21", "title": "Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.</p></article>", "keywords": ["2. Zero hunger", "yield estimation", "S", "Leaf Area Index", "EPIC model", "Agriculture", "Crop growth model", "04 agricultural and veterinary sciences", "15. Life on land", "crop growth model", "Yield estimation", "13. Climate action", "Data assimilation", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "data assimilation"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://doi.org/10.5194/egusphere-egu2020-21951"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/egusphere-egu2020-21951", "name": "item", "description": "10.5194/egusphere-egu2020-21951", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-egu2020-21951"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-21T00:00:00Z"}}, {"id": "10261/276620", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:24:37Z", "type": "Journal Article", "created": "2021-02-05", "title": "Numerically scheduling plant water deficit index-based smart irrigation to optimize crop yield and water use efficiency", "description": "Open AccessThis research was supported partly by National Key Research and Development Program of China (2016YFD0200303, 2017YFE0118100), National Natural Science Foundation of China (U1706211, 51790532), Special Fund for Scientific Research in the Public Interest (201411009), and the European Union\u2019s Horizon 2020 Research and Innovation Programme under Project SHui, grant agreement No 773903.", "keywords": ["0106 biological sciences", "2. Zero hunger", "Crop growth", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Soil water transport", "01 natural sciences", "Irrigation scheduling", "Decision support system", "Regulated deficit irrigation", "6. Clean water"]}, "links": [{"href": "https://doi.org/10261/276620"}, {"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": "10261/276620", "name": "item", "description": "10261/276620", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/276620"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-01T00:00:00Z"}}, {"id": "2945065301", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:25:54Z", "type": "Journal Article", "created": "2019-05-21", "title": "Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.</p></article>", "keywords": ["2. Zero hunger", "yield estimation", "S", "Leaf Area Index", "EPIC model", "Agriculture", "Crop growth model", "04 agricultural and veterinary sciences", "15. Life on land", "crop growth model", "Yield estimation", "13. Climate action", "Leaf area index", "Data assimilation", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "data assimilation"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://www.mdpi.com/2073-4395/9/5/255/pdf"}, {"href": "https://doi.org/2945065301"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2945065301", "name": "item", "description": "2945065301", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2945065301"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-21T00:00:00Z"}}, {"id": "10.57745/5TJJZA", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:24:09Z", "type": "Dataset", "title": "Innovative cropping systems designed to reach both environmental and production targets: data set of biotic and abiotic variables from a twelve-year French field trial.", "description": "The data set describe variables collected from a French (N 48.84\u00b0, E 1.95\u00b0) field trial, over a twelve-year period (2009-2020), in which four innovative cropping systems designed to reach multiple environmental and production goals were assessed. Both biotic and abiotic variables were measured. In a previous data paper, we focused on nitrogen (N) fluxes collected from two systems, over eight years (2009-2016). In the present one, we enlarge the scope of the variables, including more crop descriptions and environmental indicators, from the all four systems, and over a longer period (2009-2020). The biotic data are: growth stages, aboveground plant N content and biomass collected at different growth stages according to the species, yield components of all the crops, yield harvested with a combine machine, crop sequences. No weed, crop disease, and pest\u2019s data are described. The abiotic data are soil physical and chemical properties (i.e. texture, calcareous content, pH, organic carbon (C) contents, and N contents) collected at different assessment periods. All agricultural practices and climate were regularly recorded, and the treatment frequency indexes and the energy consumptions were computed. The four innovative cropping systems were designed with new combinations of agricultural practices; they differed in terms of pesticide uses, N inputs, tillage practices, and crop sequences. These data could be used for benchmark to design low-input systems, to improve models for parameterization and validation, and to increase the predictive accuracy of models of growth and development for crops, specifically orphan species as linseed, faba bean of hemp, and soil C and soil N fluxes in original conditions.", "keywords": ["Crop biomass", "Yield", "Agricultural Sciences", "Life Sciences", "Agriculture", " Forestry", " Horticulture", " Aquaculture", "cropping system", "Long-term field trial", "Agronomy", "Energy consumption", "Treatment frequency index", "Agricultural practices", "Crop growth", "Agriculture", " Forestry", " Horticulture", " Aquaculture and Veterinary Medicine", "Agricultural practices.", "Long term field trial", "Agriculture", " Forestry", " Horticulture"], "contacts": [{"organization": "Colnenne-David, Caroline", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.57745/5TJJZA"}, {"rel": "self", "type": "application/geo+json", "title": "10.57745/5TJJZA", "name": "item", "description": "10.57745/5TJJZA", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.57745/5TJJZA"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "3128569961", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:26:10Z", "type": "Journal Article", "created": "2021-02-05", "title": "Numerically scheduling plant water deficit index-based smart irrigation to optimize crop yield and water use efficiency", "description": "Open AccessThis research was supported partly by National Key Research and Development Program of China (2016YFD0200303, 2017YFE0118100), National Natural Science Foundation of China (U1706211, 51790532), Special Fund for Scientific Research in the Public Interest (201411009), and the European Union\u2019s Horizon 2020 Research and Innovation Programme under Project SHui, grant agreement No 773903.", "keywords": ["0106 biological sciences", "2. Zero hunger", "Crop growth", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Soil water transport", "01 natural sciences", "Irrigation scheduling", "Decision support system", "Regulated deficit irrigation", "6. Clean water"]}, "links": [{"href": "https://doi.org/3128569961"}, {"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": "3128569961", "name": "item", "description": "3128569961", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3128569961"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-01T00:00:00Z"}}, {"id": "58175614-c705-4e03-a249-1414015c388b", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-180.0, -90.0], [-180.0, 90.0], [180.0, 90.0], [180.0, -90.0], [-180.0, -90.0]]]}, "properties": {"themes": [{"concepts": [{"id": "climatologyMeteorologyAtmosphere"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}], "updated": "2021-06-09T07:16:45", "language": "eng", "title": "GAEZ v4 Theme 3: Agro-climatic Potential Yield - (Global - about 9 km)", "description": "Crop cultivation potential describes the agronomically possible upper limit to produce individual crops under given agro-climatic, soil and terrain conditions and applying specific management assumptions and agronomic input levels. Soil moisture conditions together with other climate characteristics (radiation and temperature) during different crop development stages are used in an eco-physiological crop growth model to calculate potential biomass production and yield. The constraint-free crop yields calculated in the AEZ biomass model reflect yield potentials with regard to temperature and radiation regimes prevailing in each grid-cell. Maximum biomass and yields depend on the timing of the crop growth cycle (crop calendar) and are separately calculated for irrigated and rain-fed conditions. \n\nCrop specific requirements are matched with temperature regimes prevailing in individual grid-cells. Matching is tested for the full range of possible starting dates. For rain-fed conditions the crop calendar resulting in the highest expected (water-limited) yield is selected to represent maximum biomass and agro-climatic potential yield of the respective crop in a particular grid-cell. The estimation of yield losses due to water stress is based on crop-specific water balances. Yield estimation for irrigation conditions assumes that irrigation is scheduled such that no yield-reducing crop water deficits occur during the crop growth cycle.\n\nDifferences in crop types and production systems are empirically characterized by the concept of Land Utilization Types (LUTs). A LUT comprises technical specifications for crop production within a given socioeconomic setting. Specific LUT attributes include agronomic information, type of the main produce, water supply type, information on typical cultivation practices, and utilization of main produce. GAEZ v4 distinguishes more than 300 crops/LUTs per level of inputs/management, which are separately assessed for rain-fed and irrigated conditions. 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