Sharps, K. et al

Yield Constraint Score (YCS) for the effect of five crop stresses on global production of four staple food crops

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° by 1° resolution, 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. 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’s 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. The YCS data were developed as part of the NERC funded SUNRISE project (NEC06476) and the National Capability Project NC-Air quality impacts on food security, ecosystems and health (NEC05574).

Publication date: 2020-07-20

Get the data

This dataset is available under the terms of the Open Government Licence

Format of the data: Shapefile

You must 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). Yield Constraint Score (YCS) for the effect of five crop stresses on global production of four staple food crops. NERC Environmental Information Data Centre. https://doi.org/10.5285/d347ed22-2b57-4dce-88e3-31a4d00d4358

 

© UK Centre for Ecology & Hydrology

© Norwegian Meteorological Institute

© University of Bonn

© Stockholm Environment Institute at York

© University of Gothenburg

© The University of Tokyo

© Banaras Hindu University

Where/When

Study area
Temporal extent
1950-01-01    to    2014-12-31

Provenance & quality

A 1° by 1° resolution grid was created using ArcMap. Crop production data (0.0833° resolution) from the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) dataset (for the year 2000) was downloaded for maize, rice, soybean and wheat. For each crop, total production was summed per 1° by 1° 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 >500 tonnes crop production were included when mapping the Yield Constraint Score (YCS).

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). Percentage yield loss per grid cell 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. 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 from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF) for 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’s Consortium for Spatial Information (CGIAR-CSI).

For ozone and pests, crop yield loss data was used to derive the YCS on a scale of 1-5, with 1 = 0-5% loss; 2 = 5- 10% loss; 3 = 10-25% loss; 4 = 25-40% loss; 5 = >40% loss. Percentage yield loss data was not available for the other 3 crop stresses, therefore categorical classes were used to designate the YCS from 1 (No/very little stress) – 5 (severe stress). The highest yield loss class (>40%) was expected to be comparable to severe stress (i.e. YCS = 5) for all yield constraints. YCS values for each crop stress, and the total YCS (i.e. sum of the score for all stresses) were added to the 1° by 1° resolution grid and saved as GIS shapefiles, with one file per crop.

Full detail on the methodology used to obtain data for each crop stress is available in the Supporting Information for this dataset.

Supplemental information

This dataset is a supplement to:

Mills, G., Sharps, K., Simpson, D., Pleijel, H., Frei, M., Burkey, K., Emberson, L., Uddling, J., Broberg, M., Feng, Z., Kobayashi, K., & Agrawal, M. (2018). Closing the global ozone yield gap: Quantification and cobenefits for multistress tolerance. Global Change Biology, 24(10), 4869–4893.

Other useful information regarding this dataset:

Simpson, D., Benedictow, A., Berge, H., Bergström, R., Emberson, L. D., Fagerli, H., Flechard, C. R., Hayman, G. D., Gauss, M., Jonson, J. E., Jenkin, M. E., Nyíri, A., Richter, C., Semeena, V. S., Tsyro, S., Tuovinen, J.-P., Valdebenito, Á., & Wind, P. (2012). The EMEP MSC-W chemical transport model – technical description. Atmospheric Chemistry and Physics, 12(16), 7825–7865.
Mills, G., Sharps, K., Simpson, D., Pleijel, H., Broberg, M., Uddling, J., … Van Dingenen, R. (2018). Ozone pollution will compromise efforts to increase global wheat production. Global Change Biology, 24(8), 3560–3574.

Correspondence/contact details

Sharps, K.
UK Centre for Ecology & Hydrology
 enquiries@ceh.ac.uk

Authors

Sharps, K.
UK Centre for Ecology & Hydrology
Mills, G.
UK Centre for Ecology & Hydrology
Simpson, D.
EMEP MSC‐W, Norwegian Meteorological Institute
Pleijel, H.
University of Gothenburg
Frei, M.
Institute of Crop Science and Resource Conservation, University of Bonn
Burkey, K.
United States Department of Agriculture – Agricultural Research Service (USDA‐ARS)
Emberson, L.
Stockholm Environment Institute at York
Uddling, J.
University of Gothenburg
Broberg, M.
University of Gothenburg
Feng, Z.
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco‐Environmental Sciences, Chinese Academy of Sciences
Kobayashi, K.
University of Tokyo
Agrawal, M.
Institute of Science, Banaras Hindu University

Other contacts

Custodian
Environmental Information Data Centre
 info@eidc.ac.uk
Publisher
NERC Environmental Information Data Centre
 info@eidc.ac.uk

Additional metadata

Topic categories
Climatology / Meteorology / Atmosphere
Keywords
Agriculture Air Pollution,  Aridity,  Crop Yield,  Diseases,  Glycine max Heat Stress,  maize,  O3,  Oryza sativa Ozone,  Pests,  rice,  Soil,  Soil soy bean,  Sunrise,  Triticum aestivum wheat,  Yield Loss,  Zea mays
INSPIRE Theme
Environmental Monitoring Facilities
Funding
Natural Environment Research Council
Natural Environment Research Council
Spatial representation type
Vector
Spatial reference system
WGS 84
Last updated
17 February 2021 16:56