2a932995-f040-4724-ad21-3e92ae8a2540 English ISO/IEC 8859-1 (also known as Latin 1) dataset dataset NERC EDS Environmental Information Data Centre Lancaster Environment Centre, Library Avenue, Bailrigg Lancaster LA1 4AP UK info@eidc.ac.uk https://eidc.ac.uk/ EIDC website The Environmental Information Data Centre (EIDC) is the UK's national data centre for terrestrial and freshwater sciences. information pointOfContact 2025-11-13T16:14:37 UK GEMINI 2.3 WGS 84 Modelled annual average percentage yield loss due to ozone damage for four global staple crops, 2010-2012 version 2 2020-07-10 publication 2017-12-15 creation https://catalogue.ceh.ac.uk/id/2a932995-f040-4724-ad21-3e92ae8a2540 10.5285/2a932995-f040-4724-ad21-3e92ae8a2540 doi: Sharps, K., Mills, G., Simpson, D., Pleijel, H., Frei, M., Burkey, K., Emberson, L., Uddling, J., Broberg, M., Feng, Z., Kobayashi, K., Agrawal, M. (2020). Modelled annual average percentage yield loss due to ozone damage for four global staple crops, 2010-2012 version 2. NERC Environmental Information Data Centre 10.5285/2a932995-f040-4724-ad21-3e92ae8a2540 Modelled average percentage yield loss due to ground-level ozone pollution (per 1 degree by 1 degree grid cell) are presented for the crops maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and wheat (Triticum aestivum) for the period 2010-2012. Data are on a global scale, based on the distribution of production for each crop, according to the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) crop production data for the year 2000. Modelled ozone data (2010-2012) needed for yield loss calculations were derived from the EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre-West) chemical transport model (version 4.16). Mapping the global crop yield losses due to ozone highlights the impact of ozone on crops and allows areas at high risk of ozone damage to be identified, which is one of the first steps towards mitigation of the problem. The yield loss calculations were done as part of the NERC funded SUNRISE project and National Capability Project NC-Air quality impacts on food security, ecosystems and health. Full details about this dataset can be found at https://doi.org/10.5285/2a932995-f040-4724-ad21-3e92ae8a2540 Sharps, K. UK Centre for Ecology & Hydrology enquiries@ceh.ac.uk https://orcid.org/0000-0003-3265-1505 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information pointOfContact Sharps, K. UK Centre for Ecology & Hydrology enquiries@ceh.ac.uk https://orcid.org/0000-0003-3265-1505 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Mills, G. UK Centre for Ecology & Hydrology enquiries@ceh.ac.uk author Simpson, D. Norwegian Meteorological Institute david.simpson@chalmers.se https://orcid.org/0000-0001-9538-3208 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Pleijel, H. University of Gothenburg hakan.pleijel@bioenv.gu.se https://orcid.org/0000-0002-6975-5984 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Frei, M. University of Bonn mfrei@uni-bonn.de https://orcid.org/0000-0002-2474-6558 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Burkey, K. United States Department of Agriculture – Agricultural Research Service (USDA‐ARS) kent.burkey@usda.gov author Emberson, L. Stockholm Environment Institute at York l.emberson@york.ac.uk author Uddling, J. University of Gothenburg johan.uddling@bioenv.gu.se author Broberg, M. University of Gothenburg malin.broberg@bioenv.gu.se https://orcid.org/0000-0001-5370-4894 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Feng, Z. Chinese Academy of Sciences zhaozhong.feng@nuist.edu.cn author Kobayashi, K. The University of Tokyo aclasman@mail.ecc.u-tokyo.ac.jp author Agrawal, M. Banaras Hindu University madhoo58@yahoo.com author NERC Environmental Information Data Centre info@eidc.ac.uk publisher NERC EDS Environmental Information Data Centre info@eidc.ac.uk custodian notPlanned Environmental Monitoring Facilities theme GEMET - INSPIRE themes, version 1.0 2008-06-01 publication Agriculture Environmental risk Triticum aestivum Glycine max Zea may Oryza sativa SUNRISE otherRestrictions no limitations otherRestrictions This resource is available under the terms of the Open Government Licence otherRestrictions © UK Centre for Ecology & Hydrology otherRestrictions © Norwegian Meteorological Institute otherRestrictions © University of Gothenburg otherRestrictions © University of Bonn otherRestrictions © United States Department of Agriculture otherRestrictions © Stockholm Environment Institute at York otherRestrictions © Chinese Academy of Sciences otherRestrictions © The University of Tokyo otherRestrictions © Banaras Hindu University otherRestrictions If you reuse this data, you should cite: Sharps, K., Mills, G., Simpson, D., Pleijel, H., Frei, M., Burkey, K., Emberson, L., Uddling, J., Broberg, M., Feng, Z., Kobayashi, K., Agrawal, M. (2020). Modelled annual average percentage yield loss due to ozone damage for four global staple crops, 2010-2012 version 2. NERC Environmental Information Data Centre https://doi.org/10.5285/2a932995-f040-4724-ad21-3e92ae8a2540 vector 100000 English utf8 farming climatologyMeteorologyAtmosphere 2010-01-01 2012-12-31 -180 180 -90 90 Shapefile NERC EDS Environmental Information Data Centre info@eidc.ac.uk distributor https://data-package.ceh.ac.uk/data/2a932995-f040-4724-ad21-3e92ae8a2540 Download the data Download a copy of this data download https://data-package.ceh.ac.uk/sd/2a932995-f040-4724-ad21-3e92ae8a2540.zip Supporting information Supporting information available to assist in re-use of this dataset information dataset dataset Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 A 1 degree by 1 degree grid was created using ArcMap. Crop production data (0.0833 degree resolution) from the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) dataset (for the year 2000) was downloaded. Irrigated and non-irrigated production data was collected for each crop type. For each crop, total production was summed per 1 degree by 1 degree grid cell. Then average production for the period 2010-2012 for each grid cell was estimated using a conversion factor from FAO national crop production data, based on the difference between average production for the period 1999-2001 and 2010-2012. For each crop, only grid cells with greater than 500 tonnes crop production were included when mapping yield loss. Each grid cell was classed as either irrigated (greater than 75 percent irrigated crop production) or non-irrigated. Grid cells were also assigned to a hemisphere (Northern or Southern) and a climatic zone. For each hemisphere/climatic zone combination, a 90-day growing period was set, based on the main growing season per year for each crop. The EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre‐West) chemical transport model (version 4.16) was used to calculate daily ozone flux (POD3IAM; phytotoxic ozone dose above 3 nmol m−2 s−1, parameterized for integrated assessment modelling) for the years 2010 - 2012. For each crop, the accumulated 90-day POD3IAM was calculated per grid cell based on the climate-specific growing period for the cell, and an average value calculated for the period 2010-2012. Yield loss was calculated using the ozone dose-response relationship for wheat, following the most recent methodology adopted by the Convention for Long-Range Transboundary Air Pollution (CLRTAP) in 2017. First, a reference value of POD3IAM = 0.1mmol/m2 (used to represent ozone uptake at pre-industrial or natural ozone levels) was subtracted before yield loss was calculated. This value was the mean POD3IAM for the experimental conditions included in the dose-response relationship, assuming constant 10 ppb ozone throughout the 90-day period. The equation used to calculate percentage yield loss was as follows: percentage Yield Loss = (POD3IAM – 0.1) * 0.64 where 0.64 is the slope of the relationship between POD3IAM and percentage yield reduction and represents the percentage reduction per mmol/m2 POD3IAM. For maize, rice and soybean, the POD3IAM values per grid cell were first used to calculate percentage yield loss using the equation for wheat. Then the relative ozone sensitivity of each crop compared to wheat was calculated by dividing the slope of the M7 (7-hour mean ozone concentration) response function for the crop by that for wheat. For each grid cell, the percentage yield loss for wheat was multiplied by the relative ozone sensitivity value, to calculate the final estimated percentage yield loss for each crop. Percentage yield loss values were added to the 1 degree by 1 degree grid and saved as GIS shapefiles, one per crop for ingestion into the EIDC. An evaluation of EMEP model performance found a strong correlation between modelled and measured ozone data from Global Atmosphere Watch (GAW) sites. The EMEP model was found to capture spatial and temporal variations in ozone across regions.