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The Scientific World Journal
Volume 2013, Article ID 851901, 7 pages
Research Article

Application of the Denitrification-Decomposition Model to Predict Carbon Dioxide Emissions under Alternative Straw Retention Methods

1College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
2Melbourne School of Land and Environment, The University of Melbourne, Melbourne, VIC 3010, Australia

Received 27 September 2013; Accepted 19 November 2013

Academic Editors: S. Ersahin and N. Tomasi

Copyright © 2013 Can Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Straw retention has been shown to reduce carbon dioxide (CO2) emission from agricultural soils. But it remains a big challenge for models to effectively predict CO2 emission fluxes under different straw retention methods. We used maize season data in the Griffith region, Australia, to test whether the denitrification-decomposition (DNDC) model could simulate annual CO2 emission. We also identified driving factors of CO2 emission by correlation analysis and path analysis. We show that the DNDC model was able to simulate CO2 emission under alternative straw retention scenarios. The correlation coefficients between simulated and observed daily values for treatments of straw burn and straw incorporation were 0.74 and 0.82, respectively, in the straw retention period and 0.72 and 0.83, respectively, in the crop growth period. The results also show that simulated values of annual CO2 emission for straw burn and straw incorporation were 3.45 t C ha−1 y−1 and 2.13 t C ha−1 y−1, respectively. In addition the DNDC model was found to be more suitable in simulating CO2 mission fluxes under straw incorporation. Finally the standard multiple regression describing the relationship between CO2 emissions and factors found that soil mean temperature (SMT), daily mean temperature (), and water-filled pore space (WFPS) were significant.