Evaluation of four modelling approaches to estimate nitrous oxide emissions in China's cropland

Qian Yue, Kun Cheng (Corresponding Author), Stephen Ogle, Jonathan Hillier, Pete Smith, Mohamed Abdalla, Jianfei Sun, Genxing Pan

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Process-based models are useful tools to integrate the effects of detailed agricultural practices, soil characteristics, mass balance, and climate change on soil N2O emissions in soil - plant ecosystems, whereas static, seasonal or annual models often exist to estimate cumulative N2O emissions under data-limited conditions. A study was carried out to compare the capability of four models to estimate seasonal cumulative fluxes from 425 field measurements of N2O emissions representing 67 studies across China’s croplands. The models were 1) the daily time-step version of CENTURY (DAYCENT), 2) DeNitrification - DeComposition model (DNDC), 3) the linear regression model (LRM) of Yue et al. (2018), and 4) IPCC Tier 1 emission factors. The DAYCENT and DNDC models were estimated crop yields with R2 values of 0.60 and 0.66 respectively; but DNDC showed significant underestimation according to bias analysis. For seasonal cumulative N2O emission predictions, the correlation of modelled with measured N2O emissions had an R2 of 0.14, 0.14, 0.23 and 0.15 for DAYCENT, DNDC, LRM of Yue et al. (2018), and IPCC, respectively. No significant bias was identified except for the significant underestimation of 0.52 kg N2O-N ha-1 with the DNDC model. The modelled daily N2O emission against observations from the experimental fields indicated that the DAYCENT and DNDC models simulated temporal patterns effectively, although they did not capture the emission peaks perfectly. Based on RMSE and bias analysis, LRM performed well on N2O emission prediction for paddy rice fields, while DAYCENT performed well for wheat and IPCC for maize. All models simulated N2O fluxes well for soybeans, but not well for cotton or fallow. Moreover, DAYCENT and LRM performed well under different fertilizer management (no fertilizer, mineral fertilizer, and organic fertilizer), while DNDC significantly underestimated the emissions under no fertilizer and when organic fertilizer was applied, as did IPCC when organic fertilizer was applied.
Original languageEnglish
Pages (from-to)1279-1289
Number of pages11
JournalScience of the Total Environment
Volume652
Early online date26 Oct 2018
DOIs
Publication statusPublished - 20 Feb 2019

Fingerprint

Nitrous Oxide
nitrous oxide
Oxides
modeling
Denitrification
Fertilizers
denitrification
fertilizer
decomposition
Decomposition
Linear regression
evaluation
Soils
Fluxes
soil emission

Keywords

  • nitrous oxide
  • model simulation
  • cropland
  • DAYCENT
  • DNDC
  • linear model
  • Cropland
  • Linear regression model
  • Nitrous oxide
  • Model simulation

ASJC Scopus subject areas

  • Pollution
  • Waste Management and Disposal
  • Environmental Engineering
  • Environmental Chemistry

Cite this

Evaluation of four modelling approaches to estimate nitrous oxide emissions in China's cropland. / Yue, Qian; Cheng, Kun (Corresponding Author); Ogle, Stephen; Hillier, Jonathan; Smith, Pete; Abdalla, Mohamed; Sun, Jianfei; Pan, Genxing.

In: Science of the Total Environment, Vol. 652, 20.02.2019, p. 1279-1289.

Research output: Contribution to journalArticle

Yue, Qian ; Cheng, Kun ; Ogle, Stephen ; Hillier, Jonathan ; Smith, Pete ; Abdalla, Mohamed ; Sun, Jianfei ; Pan, Genxing. / Evaluation of four modelling approaches to estimate nitrous oxide emissions in China's cropland. In: Science of the Total Environment. 2019 ; Vol. 652. pp. 1279-1289.
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abstract = "Process-based models are useful tools to integrate the effects of detailed agricultural practices, soil characteristics, mass balance, and climate change on soil N2O emissions in soil - plant ecosystems, whereas static, seasonal or annual models often exist to estimate cumulative N2O emissions under data-limited conditions. A study was carried out to compare the capability of four models to estimate seasonal cumulative fluxes from 425 field measurements of N2O emissions representing 67 studies across China’s croplands. The models were 1) the daily time-step version of CENTURY (DAYCENT), 2) DeNitrification - DeComposition model (DNDC), 3) the linear regression model (LRM) of Yue et al. (2018), and 4) IPCC Tier 1 emission factors. The DAYCENT and DNDC models were estimated crop yields with R2 values of 0.60 and 0.66 respectively; but DNDC showed significant underestimation according to bias analysis. For seasonal cumulative N2O emission predictions, the correlation of modelled with measured N2O emissions had an R2 of 0.14, 0.14, 0.23 and 0.15 for DAYCENT, DNDC, LRM of Yue et al. (2018), and IPCC, respectively. No significant bias was identified except for the significant underestimation of 0.52 kg N2O-N ha-1 with the DNDC model. The modelled daily N2O emission against observations from the experimental fields indicated that the DAYCENT and DNDC models simulated temporal patterns effectively, although they did not capture the emission peaks perfectly. Based on RMSE and bias analysis, LRM performed well on N2O emission prediction for paddy rice fields, while DAYCENT performed well for wheat and IPCC for maize. All models simulated N2O fluxes well for soybeans, but not well for cotton or fallow. Moreover, DAYCENT and LRM performed well under different fertilizer management (no fertilizer, mineral fertilizer, and organic fertilizer), while DNDC significantly underestimated the emissions under no fertilizer and when organic fertilizer was applied, as did IPCC when organic fertilizer was applied.",
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