Spatial and temporal uncertainty of crop yield aggregations |
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| Institution: | 1. Potsdam Institute for Climate Impact Research, Research Domain II Climate Impacts & Vulnerabilities, 14473 Potsdam, Germany;2. Columbia University Center for Climate Systems Research, New York, NY 10025, USA;3. University of Chicago and ANL Computation Institute, Chicago, IL 60637, USA;4. National Agriculture and Research Organization, Institute for Agro-Environmental Sciences, Tsukuba, 305-8604, Japan;5. University of Minnesota, Institute on the Environment, Saint Paul, MN 55108, USA;6. NASA Goddard Institute for Space Studies, New York, NY 10025, USA;7. Karlsruhe Institute of Technology, IMK-IFU, 82467 Garmisch-Partenkirchen, Germany;8. International Institute for Applied Systems Analysis, Ecosystem Services and Management Program, 2361 Laxenburg, Austria;9. Comenius University in Bratislava, Department of Soil Science, 842 15 Bratislava, Slovak Republic;10. Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ, Orme des Merisiers, 91191 Gif-sur-Yvette, France;11. Ludwig Maximilian University, Department of Geography, 80333 Munich, Germany;12. University of Maryland, Department of Geographical Sciences, College Park, MD 20742, USA;13. Texas A&M University, Texas AgriLife Research and Extension, Temple, TX 76502, USA;14. National Center for Atmospheric Research, Earth System Laboratory, Boulder, CO 80307, USA;15. Swiss Federal Institute of Aquatic Science and Technology, Eawag, CH-8600 Duebendorf, Switzerland;p. University of Birmingham, School of Geography, Earth & Environmental Science and Birmingham Institute of Forest Research, B15 2TT Birmingham, United Kingdom;q. University of Natural Resources and Life Sciences, Institute for Sustainable Economic Development, 1180 Vienna, Austria;r. Peking University, Sino-French Institute of Earth System Sciences, 100871 Beijing, China;s. Alterra Wageningen University and Research Centre, Earth Observation and Environmental Informatics, 6708PB Wageningen, Netherlands;1. ULg Gembloux Agro-Bio Tech, Department of Environmental Sciences and Technologies, 5030 Gembloux, Belgium;2. Department of Geological Sciences and W.K. Kellogg Biological Station, Michigan State University, USA;3. ULg Gembloux Agro-Bio Tech, Department of Agronomical Sciences, 5030 Gembloux, Belgium;4. ULg Gembloux Agro-Bio Tech, Walloon Agricultural Research Centre (CRA-W), 5030 Gembloux, Belgium;1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China;2. CSIRO Agriculture and Food, GPO Box 1700, Canberra, ACT 2601, Australia;3. CSIRO Agriculture and Food, Private Bag 5, Wembley WA 6913, Australia;1. Università degli Studi di Milano, DEMM, Cassandra Lab, via Celoria 2, 20133 Milan, Italy;2. Università degli Studi di Milano, DISAA, Cassandra Lab, via Celoria 2, 20133 Milan, Italy;1. Department of Plant and Soil Sciences, 371 Agricultural Hall, Stillwater, OK, USA;2. International Maize and Wheat Improvement Center, Apdo. Postal 6-641, 06600 Mexico, D.F., Mexico;3. Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352, USA;4. Commonwealth Scientific and Industrial Research Organisation, 41 Boggo Road, Dutton Park, QLD, Australia;1. INRA, UMR AGIR, Toulouse, France;2. Department of Crop Science, Faculty of Agriculture, University of Peradeniya, Sri Lanka;3. Faculty of Agricultural Sciences, Sabaragamuwa University of Sri Lanka, Belihuloya 70140, Sri Lanka;4. Field Crops Research and Development Institute, Mahailluppallma, Sri Lanka;5. CSIRO Agriculture Flagship, GPO Box 2583, Brisbane, QLD 4001, Australia;6. Department of Agriculture, University of Florida, United States;7. Agricultural and Biological Engineering Department, University of Florida, United States;1. Agriculture and Agri-Food Canada, Soils and Crops Research and Development Centre, 2560 Hochelaga Blvd., Quebec City, QC G1V 2J3, Canada;2. Agriculture and Agri-Food Canada, Eastern Cereal and Oilseed Research Centre, 960 Carling Ave., Ottawa, ON K1A 0C6, Canada;3. La Financière agricole du Québec, 1400 De la Rive-Sud Blvd., St-Romuald, QC G6W 8K7, Canada |
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| Abstract: | The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty.The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics.Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05 (wheat, Russia), r = 0.13 (rice, Vietnam), and r = ?0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises. |
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| Keywords: | Aggregation uncertainty Global crop model Crop yields Gridded data Harvested area |
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