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Using DNDC models with two assimilation algorithms to simulate N2O emissions from a farmland after biochar added

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  • Chen, Can
  • Wang, Kexin
  • Zhu, Hongxia

Abstract

As a greenhouse gas in the atmosphere, N2O has been widely concerned by scientists because of its high warming potential. Using modeling methods to simulate N2O emissions from farmland is the main one of current research approaches. In this study, the coupled assimilation method is used to compare the Denitrification-Decomposition (DNDC) model simulation results with the measured data. The results were as follows: (1) The DNDC model without assimilation has poor effectiveness in simulating the daily N2O emission flux and total N2O emissions from farmland after biochar added. The simulation accuracy of N2O emissions is: r = 0.677, R2 = 0.458, RMSE = 0.892; (2) The DNDC model, assimilated by simulating annealing (SA) algorithm and Bayesian inference (BI) algorithm, has significantly improved the simulation accuracy of daily N2O emissions flux and total N2O emissions from farmland after biochar added. The SA algorithm simulation accuracy of N2O emission is: r = 0.964, R2 = 0.929, RMSE = 0.18. The BI algorithm simulation accuracy of N2O emission is: r = 0.917, R2 = 0.841, RMSE = 0.29. From the uncertainty analysis of this study, it could be inferred that 50 % of the calibration points could already meet the DNDC simulation accuracy requirements for the assimilation algorithm. The simulation accuracy of SA algorithm after assimilation is higher than BI algorithm. The results of this study provide scientific data for using the DNDC model to increase the accuracy of simulating N2O emissions after biochar added.

Suggested Citation

  • Chen, Can & Wang, Kexin & Zhu, Hongxia, 2025. "Using DNDC models with two assimilation algorithms to simulate N2O emissions from a farmland after biochar added," Ecological Modelling, Elsevier, vol. 510(C).
    • Handle: RePEc:eee:ecomod:v:510:y:2025:i:c:s0304380025002650
    DOI: 10.1016/j.ecolmodel.2025.111279
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    References listed on IDEAS

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    1. Hanane Menad & Farah Ben-Naoum & Abdelmalek Amine, 2019. "A Thresholding Approach for Pollen Detection in Images Based on Simulated Annealing Algorithm," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global Scientific Publishing, vol. 10(4), pages 18-36, October.
    2. Gheisari, Aboalghasem & Asgharipour, Mohammad Reza & Mousavi-Nik, Mohsen & Ghanbari, Ahmad & Javaheri, Mohammad Ali, 2023. "Utilization of the DNDC model to estimate yield and CO2 and CH4 emissions in a cotton-wheat rotation under the influence of various tillage treatments," Ecological Modelling, Elsevier, vol. 481(C).
    3. R.B. Arellano-Valle & H. Bolfarine & V.H. Lachos, 2007. "Bayesian Inference for Skew-normal Linear Mixed Models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(6), pages 663-682.
    4. Shaukat, Muhammad & Muhammad, Sher & Maas, Ellen D.V.L. & Khaliq, Tasneem & Ahmad, Ashfaq, 2022. "Predicting methane emissions from paddy rice soils under biochar and nitrogen addition using DNDC model," Ecological Modelling, Elsevier, vol. 466(C).
    5. Li, Hu & Wang, Ligang & Li, Jianzheng & Gao, Maofang & Zhang, Jing & Zhang, Jianfeng & Qiu, Jianjun & Deng, Jia & Li, Changsheng & Frolking, Steve, 2017. "The development of China-DNDC and review of its applications for sustaining Chinese agriculture," Ecological Modelling, Elsevier, vol. 348(C), pages 1-13.
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