{"type": "FeatureCollection", "features": [{"id": "10.1007/s004420100656", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:15:07Z", "type": "Journal Article", "created": "2003-02-13", "title": "Fine-Root Biomass And Fluxes Of Soil Carbon In Young Stands Of Paper Birch And Trembling Aspen As Affected By Elevated Atmospheric Co2 And Tropospheric O3", "description": "Rising atmospheric CO2 may stimulate future forest productivity, possibly increasing carbon storage in terrestrial ecosystems, but how tropospheric ozone will modify this response is unknown. Because of the importance of fine roots to the belowground C cycle, we monitored fine-root biomass and associated C fluxes in regenerating stands of trembling aspen, and mixed stands of trembling aspen and paper birch at FACTS-II, the Aspen FACE project in Rhinelander, Wisconsin. Free-air CO2 enrichment (FACE) was used to elevate concentrations of CO2 (average enrichment concentration 535\u00a0\u00b5l l-1) and O3 (53\u00a0nl l-1) in developing forest stands in 1998 and 1999. Soil respiration, soil pCO2, and dissolved organic carbon in soil solution (DOC) were monitored biweekly. Soil respiration was measured with a portable infrared gas analyzer. Soil pCO2 and DOC samples were collected from soil gas wells and tension lysimeters, respectively, at depths of 15, 30, and 125\u00a0cm. Fine-root biomass averaged 263\u00a0g m-2 in control plots and increased 96% under elevated CO2. The increased root biomass was accompanied by a 39% increase in soil respiration and a 27% increase in soil pCO2. Both soil respiration and pCO2 exhibited a strong seasonal signal, which was positively correlated with soil temperature. DOC concentrations in soil solution averaged ~12\u00a0mg l-1 in surface horizons, declined with depth, and were little affected by the treatments. A simplified belowground C budget for the site indicated that native soil organic matter still dominated the system, and that soil respiration was by far the largest flux. Ozone decreased the above responses to elevated CO2, but effects were rarely statistically significant. We conclude that regenerating stands of northern hardwoods have the potential for substantially greater C input to soil due to greater fine-root production under elevated CO2. Greater fine-root biomass will be accompanied by greater soil C efflux as soil respiration, but leaching losses of C will probably be unaffected.", "keywords": ["0106 biological sciences", "Ecology and Evolutionary Biology", "Aspen-FACE-project", "root-", "USA-", "pollutants-", "Environmental-Sciences)", "tropospheric-ozone", "forest-productivity", "01 natural sciences", "biomass-", "northern-forests", "124-38-9: CARBON DIOXIDE", "soil-carbon-flux", "terrestrial-ecosystems", "populus-tremuloides", "Cellular and Developmental Biology", "soil-carbon", "7440-44-0: CARBON", "carbon-", "fine-root", "Bioenergetics- (Biochemistry-and-Molecular-Biophysics)", "Natural Resources and Environment", "04 agricultural and veterinary sciences", "GLOBAL-ECOLOGY", "North-America", "Nearctic-region)", "Rhinelander- (Wisconsin-", "carbon-sequestration", "atmosphere-", "biomass-production", "dissolved-organic-carbon [DOC-]", "Science", "respiration-", "carbon-dioxide-enrichment", "forest-plantations", "carbon-dioxide", "carbon-storage", "fine-root-biomass", "belowground-biomass", "United-States-Wisconsin-Rhinelander", "carbon-cycle", "Health Sciences", "ozone-", "soil-respiration", "air-pollution", "global-change", "atmospheric-carbon-dioxide", "biomass", "Molecular", "15. Life on land", "ozone", "13. Climate action", "roots-", "Legacy", "Terrestrial-Ecology (Ecology-", "free-air-carbon-dioxide-enrichment [FREE-]: experimental-method", "0401 agriculture", " forestry", " and fisheries", "Northern Forests Global Change Carbon Sequestration Soil Respiration Dissolved Organic Carbon Soil PCO2"]}, "links": [{"href": "https://doi.org/10.1007/s004420100656"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Oecologia", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s004420100656", "name": "item", "description": "10.1007/s004420100656", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s004420100656"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2001-07-01T00:00:00Z"}}, {"id": "10.1029/2017JD027827", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:18:15Z", "type": "Journal Article", "created": "2018-04-26", "title": "Fine Particle Emissions From Tropical Peat Fires Decrease Rapidly With Time Since Ignition", "description": "Abstract<p>Southeast Asia experiences frequent fires in fuel\uffe2\uff80\uff90rich tropical peatlands, leading to extreme episodes of regional haze with high concentrations of fine particulate matter (PM2.5) impacting human health. In a study published recently, the first field measurements of PM2.5 emission factors for tropical peat fires showed larger emissions than from other fuel types. Here we report even higher PM2.5 emission factors, measured at newly ignited peat fires in Malaysia, suggesting that current estimates of fine particulate emissions from peat fires may be underestimated by a factor of 3 or more. In addition, we use both field and laboratory measurements of burning peat to provide the first mechanistic explanation for the high variability in PM2.5 emission factors, demonstrating that buildup of a surface ash layer causes the emissions of PM2.5 to decrease as the peat fire progresses. This finding implies that peat fires are more hazardous (in terms of aerosol emissions) when first ignited than when still burning many days later. Varying emission factors for PM2.5 also have implications for our ability to correctly model the climate and air quality impacts downwind of the peat fires. For modelers able to implement a time\uffe2\uff80\uff90varying emission factor, we recommend an emission factor for PM2.5 from newly ignited tropical peat fires of 58\uffc2\uffa0g of PM2.5 per kilogram of dry fuel consumed (g/kg), reducing exponentially at a rate of 9%/day. If the age of the fire is unknown or only a single value may be used, we recommend an average value of 24\uffc2\uffa0g/kg.</p>", "keywords": ["5", "550", "TRACE GASES", "PM2", "PM2.5", "Social and Behavioral Sciences", "01 natural sciences", "TRANSFORM INFRARED-SPECTROSCOPY", "INDONESIA", "CARBON", "SDG 3 - Good Health and Well-being", "11. Sustainability", "Medicine and Health Sciences", "Meteorology & Atmospheric Sciences", "AUSTRALIAN VEGETATION FIRES", "Research Articles", "0105 earth and related environmental sciences", "Science & Technology", "GE", "emissions", "AIR-POLLUTION", "15. Life on land", "FOREST", "FIELD-MEASUREMENTS", "DERIVATION", "13. Climate action", "Physical Sciences", "PREMATURE MORTALITY", "peat", "FoR 0401 (Atmospheric Sciences)", "FoR 0502 (Environmental Science and Management)", "fire"]}, "links": [{"href": "https://researchonline.ljmu.ac.uk/id/eprint/9303/1/Fine%20Particle%20Emissions%20From%20Tropical%20Peat%20Fires%20Decrease%20Rapidly%20With%20Time%20Since%20Ignition..pdf"}, {"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2017JD027827"}, {"href": "https://doi.org/10.1029/2017JD027827"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Atmospheres", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1029/2017JD027827", "name": "item", "description": "10.1029/2017JD027827", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2017JD027827"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-05-16T00:00:00Z"}}, {"id": "10.1071/wf17084", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:18:45Z", "type": "Journal Article", "created": "2018-05-21", "title": "Review of emissions from smouldering peat fires and their contribution to regional haze episodes", "description": "<p>  Smouldering peat fires, the largest fires on Earth in terms of fuel consumption, are reported in six continents and are responsible for regional haze episodes. Haze is the large-scale accumulation of smoke at low altitudes in the atmosphere. It decreases air quality, disrupts transportation and causes health emergencies. Research on peat emissions and haze is modest at best and many key aspects remain poorly understood. Here, we compile an up-to-date inter-study of peat fire emission factors (EFs) found in the literature both from laboratory and from field studies. Tropical peat fires yield larger EFs for the prominent organic compounds than boreal and temperate peat fires, possibly due to the higher fuel carbon content (56.0 vs 44.2%). In contrast, tropical peat fires present slightly lower EFs for particulate matter with diameter \uffe2\uff89\uffa42.5\uffe2\uff80\uff89\uffce\uffbcm (PM2.5) for unknown reasons but are probably related to combustion dynamics. An analysis of the modified combustion efficiency, a parameter widely used for determining the combustion regime of wildfires, shows it is partially misunderstood and highly sensitive to unknown field variables. This is the first review of the literature on smouldering peat emissions. Our integration of the existing literature allows the identification of existing gaps in knowledge and is expected to accelerate progress towards mitigation strategies. </p>", "keywords": ["emission factor", "550", "TRACE GASES", "CENTRAL KALIMANTAN", "01 natural sciences", "7. Clean energy", "TRANSFORM INFRARED-SPECTROSCOPY", "2015 EL-NINO", "CROP RESIDUE", "COMBUSTION", "11. Sustainability", "CHEMICAL-CHARACTERIZATION", "0105 earth and related environmental sciences", "Science & Technology", "0602 Ecology", "Forestry", "AIR-POLLUTION", "15. Life on land", "FIELD-MEASUREMENTS", "modified combustion efficiency", "FOREST-FIRES", "smoke", "13. Climate action", "FLIGHT MASS-SPECTROMETRY", "0705 Forestry Sciences", "wildfires", "0502 Environmental Science And Management", "Life Sciences & Biomedicine", "BIOMASS-BURNING EMISSIONS", "BROWN CARBON"]}, "links": [{"href": "https://www.publish.csiro.au/WF/pdf/WF17084"}, {"href": "https://doi.org/10.1071/wf17084"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Wildland%20Fire", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1071/wf17084", "name": "item", "description": "10.1071/wf17084", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1071/wf17084"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-01T00:00:00Z"}}, {"id": "10.5194/essd-13-367-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:22:36Z", "type": "Journal Article", "created": "2021-02-13", "title": "Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO): global and European emission temporal profile maps for atmospheric chemistry modelling", "description": "<p>Abstract. We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx; SOx; NMVOC, non-methane volatile organic compound; NH3; CO; PM10; and PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1\uffe2\uff80\uff89\uffc3\uff97\uffe2\uff80\uff890.1\uffe2\uff88\uff98 and regional European 0.1\uffe2\uff80\uff89\uffc3\uff97\uffe2\uff80\uff890.05\uffe2\uff88\uff98 grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS programme. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production and traffic counts) at national and local levels were collected and combined with existing meteorology-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily and hourly) the resulting profiles are pollutant-dependent, year-dependent (i.e. time series from 2010 to 2017) and/or spatially dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles, and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147, Guevara et al., 2020a) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68, Guevara et al., 2020b) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: February 2021).                     </p>", "keywords": ["China", "Atmospheric chemistry", "550", "Anthropogenic emissions", "Ammonia emissions", "Urbanisation", "Environment", "7. Clean energy", "[SDU] Sciences of the Universe [physics]", "11. Sustainability", "Air-pollution", "GE1-350", "Gridded emissions", "Fuel use", "QE1-996.5", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Inventory", "Geology", "Environmental sciences", "Data product", "Qu\u00edmica atmosf\u00e8rica", "13. Climate action", "Air quality", "Transport model", "Data sets", "Bottom-up", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Air pollutants"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/367/2021/essd-13-367-2021.pdf"}, {"href": "https://doi.org/10.5194/essd-13-367-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-13-367-2021", "name": "item", "description": "10.5194/essd-13-367-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-13-367-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-12T00:00:00Z"}}, {"id": "2117/342462", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:26:39Z", "type": "Journal Article", "created": "2021-02-13", "title": "Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO): global and European emission temporal profile maps for atmospheric chemistry modelling", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx; SOx; NMVOC, non-methane volatile organic compound; NH3; CO; PM10; and PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1\u2009\u00d7\u20090.1\u2218 and regional European 0.1\u2009\u00d7\u20090.05\u2218 grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS programme. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production and traffic counts) at national and local levels were collected and combined with existing meteorology-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily and hourly) the resulting profiles are pollutant-dependent, year-dependent (i.e. time series from 2010 to 2017) and/or spatially dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles, and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147, Guevara et al., 2020a) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68, Guevara et al., 2020b) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: February 2021).</p></article>", "keywords": ["China", "Atmospheric chemistry", "550", "Anthropogenic emissions", "Ammonia emissions", "Urbanisation", "Environment", "7. Clean energy", "[SDU] Sciences of the Universe [physics]", "11. Sustainability", "Air-pollution", "GE1-350", "Gridded emissions", "Fuel use", "QE1-996.5", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Inventory", "Geology", "Environmental sciences", "Data product", "Qu\u00edmica atmosf\u00e8rica", "13. Climate action", "Air quality", "Transport model", "Data sets", "Bottom-up", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Air pollutants"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/367/2021/essd-13-367-2021.pdf"}, {"href": "https://doi.org/2117/342462"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/342462", "name": "item", "description": "2117/342462", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/342462"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-12T00:00:00Z"}}, {"id": "3129610671", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:27:18Z", "type": "Journal Article", "created": "2021-02-13", "title": "Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO): global and European emission temporal profile maps for atmospheric chemistry modelling", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx; SOx; NMVOC, non-methane volatile organic compound; NH3; CO; PM10; and PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1\u2009\u00d7\u20090.1\u2218 and regional European 0.1\u2009\u00d7\u20090.05\u2218 grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS programme. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production and traffic counts) at national and local levels were collected and combined with existing meteorology-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily and hourly) the resulting profiles are pollutant-dependent, year-dependent (i.e. time series from 2010 to 2017) and/or spatially dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles, and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147, Guevara et al., 2020a) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68, Guevara et al., 2020b) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: February 2021).                     </p></article>", "keywords": ["China", "Atmospheric chemistry", "550", "Anthropogenic emissions", "Ammonia emissions", "Urbanisation", "Environment", "7. Clean energy", "[SDU] Sciences of the Universe [physics]", "11. Sustainability", "Air-pollution", "GE1-350", "Gridded emissions", "Fuel use", "QE1-996.5", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Inventory", "Geology", "Environmental sciences", "Data product", "Qu\u00edmica atmosf\u00e8rica", "13. Climate action", "Air quality", "Transport model", "Data sets", "Bottom-up", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Air pollutants"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/367/2021/essd-13-367-2021.pdf"}, {"href": "https://doi.org/3129610671"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3129610671", "name": "item", "description": "3129610671", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3129610671"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-12T00:00:00Z"}}, {"id": "85fbf5b53e47d530fdc985c6735acce7", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:35:38Z", "type": "Dataset", "title": "Yield Constraint Score (YCS) for the effect of five crop stresses on global production of four staple food crops", "description": "A Yield Constraint Score (YCS; scale of 1-5) was developed for the effect of five key crop stresses (ozone, pests and diseases, soil nutrients, heat stress and aridity) on the production of the crops maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and wheat (Triticum aestivum). Data are on a global scale at 1\u00c2\u00b0 by 1\u00c2\u00b0 resolution, based on the distribution of production for each crop, according to the Food and Agriculture Organisation\u00e2\u0080\u0099s (FAO) Global Agro-Ecological Zones (GAEZ) crop production data for the year 2000. To derive the YCS for each crop stress, spatial data on a global scale were gathered. Modelled ozone data (2010-2012) were derived from the EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre-West) chemical transport model (version 4.16). Pests and diseases data (2002-2004) were downloaded from a Centre for Agriculture and Biosciences International (CABI) database providing estimates for pre-harvest crop losses due to weeds, animal, pathogens and viruses, compiled from the literature. Soil nutrient classifications (for 2009, derived using soil attributes from the Harmonized World Soil Database (HWSD)) were downloaded from the GAEZ data portal. A heat stress index was calculated using daily temperature data (1990-2014) to determine whether the temperature within a 30-day thermal-sensitive period exceeded crop tolerance thresholds. Global Aridity Index data (1950-2000) were downloaded from the Consultative Group for International Agricultural Research\u00e2\u0080\u0099s Consortium for Spatial Information (CGIAR-CSI). The Yield Constraint Score provides an indication of where each stress is predicted to be affecting crop yield globally and the magnitude of the effect. 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