English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse
  1. Item Summary

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsStatisticsRelease HistoryDetailsSummary

Released
Journal Article

Simulation of global crop production with the ecosystem model DayCent

MPS-Authors
/persons/resource/persons37344

Stehfest,  Elke
IMPRS on Earth System Modelling, MPI for Meteorology, Max Planck Society;
The Land in the Earth System, MPI for Meteorology, Max Planck Society;

/persons/resource/persons37177

Heistermann,  Maik
External Organizations;
IMPRS on Earth System Modelling, MPI for Meteorology, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Stehfest, E., Heistermann, M., Priess, J. A., Ojima, D. S., & Alcamo, J. (2007). Simulation of global crop production with the ecosystem model DayCent. Ecological Modelling, 209(2-4), 203-219. doi:10.1016/j.ecolmodel.2007.06.028.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0015-3D68-F
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. (C) 2007 Elsevier B.V. All rights reserved.