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Simulation of global crop production with the ecosystem model DayCent

Author

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  • Stehfest, Elke
  • Heistermann, Maik
  • Priess, Joerg A.
  • Ojima, Dennis S.
  • Alcamo, Joseph

Abstract

Agriculture has become a key element within the earth system as it changes global biogeochemical and water cycles, while global environmental change affects land productivity and thus future land-use decisions. To address these issues and their complex interdependency in a consistent modelling approach we adapted the agro-ecosystem model DayCent for the simulation of major crops at the global scale. Based on a global compilation of environmental and management data and an algorithm to calculate global planting dates, DayCent was parameterised and calibrated to simulate global yield levels for wheat, maize, rice and soybeans. Simulation results show that the DayCent model is able to reproduce the major effects of climate, soil and management on crop production. Average simulated crop yield per country agree well with agricultural statistics (Modelling efficiency is about 0.66 for wheat, rice and maize, and 0.32 for soybean) and spatial patterns of yields generally correspond to observed crop distributions and sub-national census data.

Suggested Citation

  • Stehfest, Elke & Heistermann, Maik & Priess, Joerg A. & Ojima, Dennis S. & Alcamo, Joseph, 2007. "Simulation of global crop production with the ecosystem model DayCent," Ecological Modelling, Elsevier, vol. 209(2), pages 203-219.
    • Handle: RePEc:eee:ecomod:v:209:y:2007:i:2:p:203-219
    DOI: 10.1016/j.ecolmodel.2007.06.028
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    2. Cheng, Kun & Ogle, Stephen M. & Parton, William J. & Pan, Genxing, 2013. "Predicting methanogenesis from rice paddies using the DAYCENT ecosystem model," Ecological Modelling, Elsevier, vol. 261, pages 19-31.
    3. Ortiz-Bobea, Ariel & Kim, Do-Hyung & Chen, Yanyou, "undated". "Identifying climatic constraints of US agriculture," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170674, Agricultural and Applied Economics Association.
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    13. Tatsumi, Kenichi, 2016. "Effects of automatic multi-objective optimization of crop models on corn yield reproducibility in the U.S.A," Ecological Modelling, Elsevier, vol. 322(C), pages 124-137.
    14. Verena Seufert & Gustaf Granath & Christoph Müller, 2019. "A meta-analysis of crop response patterns to nitrogen limitation for improved model representation," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-25, October.
    15. Yang, Jia & Ren, Wei & Ouyang, Ying & Feng, Gary & Tao, Bo & Granger, Joshua J. & Poudel, Krishna P., 2019. "Projection of 21st century irrigation water requirement across the Lower Mississippi Alluvial Valley," Agricultural Water Management, Elsevier, vol. 217(C), pages 60-72.
    16. Baral, Nawa Raj & Mishra, Shruti K. & George, Anthe & Gautam, Sagar & Mishra, Umakant & Scown, Corinne D., 2022. "Multifunctional landscapes for dedicated bioenergy crops lead to low-carbon market-competitive biofuels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    17. Warut Pannakkong & Parthana Parthanadee & Jirachai Buddhakulsomsiri, 2022. "Impacts of Harvesting Age and Pricing Schemes on Economic Sustainability of Cassava Farmers in Thailand under Market Uncertainty," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    18. Prem S. Bindraban & Rudy Rabbinge, 2011. "European food and agricultural strategy for 21st century," International Journal of Agricultural Resources, Governance and Ecology, Inderscience Enterprises Ltd, vol. 9(1/2), pages 80-101.
    19. Dzotsi, K.A. & Basso, B. & Jones, J.W., 2015. "Parameter and uncertainty estimation for maize, peanut and cotton using the SALUS crop model," Agricultural Systems, Elsevier, vol. 135(C), pages 31-47.
    20. Qiao, Shengchao & Harrison, Sandy P. & Prentice, I. Colin & Wang, Han, 2023. "Optimality-based modelling of wheat sowing dates globally," Agricultural Systems, Elsevier, vol. 206(C).
    21. Fritz, Steffen & See, Linda & Bayas, Juan Carlos Laso & Waldner, François & Jacques, Damien & Becker-Reshef, Inbal & Whitcraft, Alyssa & Baruth, Bettina & Bonifacio, Rogerio & Crutchfield, Jim & Rembo, 2019. "A comparison of global agricultural monitoring systems and current gaps," Agricultural Systems, Elsevier, vol. 168(C), pages 258-272.
    22. Neumann, Kathleen & Verburg, Peter H. & Stehfest, Elke & Müller, Christoph, 2010. "The yield gap of global grain production: A spatial analysis," Agricultural Systems, Elsevier, vol. 103(5), pages 316-326, June.
    23. Luoman Pu & Shuwen Zhang & Jiuchun Yang & Liping Chang & Shuting Bai, 2019. "Spatio-Temporal Dynamics of Maize Potential Yield and Yield Gaps in Northeast China from 1990 to 2015," IJERPH, MDPI, vol. 16(7), pages 1-18, April.

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