EGU24-7642, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Accurate simulation of greenhouse gas emissions across fertilizer scenarios with DNDC

Tulasi Thentu1, Daniel Forster2, Perttu Virkajärvi1, Matthew Tom Harrison3, and Narasinha Shurpali1
Tulasi Thentu et al.
  • 1Natural Resources Institute Finland (LUKE), Halolantie 35 A, 71750 Maaninka, Finland.
  • 2Toho University, Narashino Campus 2-2-1 Miyama, Funabashi, Chiba 274-8510, Japan (
  • 3Tasmanian Institute of Agriculture, University of Tasmania, Launceston TAS 7248, Australia (

The aim of this paper was to compare effects of organic and mineral fertilizers on greenhouse gas (GHG) emissions from legume grasslands in Finland. We invoke DNDC, a process-based model that integrates effects of agricultural practices, soil characteristics, nitrogen mass balance and climate change on GHG emissions from soil-plant ecosystems. Data measured in the field were collected from 2017 to 2020 using an eddy covariance site cultivated with legume grass species (Phleum pratense L., Festuca pratensis Huds, Trifolium pratense L., Hordeum vulgare L.) at Anttila, Maaninka, eastern Finland. The focus of the modelling was to evaluate the performance of DNDC heat exchange version under two distinct management practices: organic input, utilizing digestate residue (slurry), and mineral input (NPK) with chemical fertilizer. The primary emphasis was on understanding the model's accuracy in simulating greenhouse gas emissions and comparing the total annual greenhouse gas exchanges between these two management approaches. The DNDC heat exchange model version was calibrated and validated for key processes, including Gross Primary Productivity (GPP), Net Ecosystem Exchange (NEE), Ecosystem Respiration (Reco), Soil Temperature, and Water-Filled Pore Space (WFPS) at 5 cm and 20 cm depths. The model demonstrated satisfactory performance in estimating the total annual GHG exchanges during validation years under both management practices. For the mineral treatment, the model demonstrated fair performance (Spearman's correlation (ρ) for GPP (0.81), NEE (0.72), and Reco (0.85)). Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values indicated reasonable agreement between model predictions and measured data. Notably, soil temperature simulations demonstrated an excellent correlation (ρ=0.99) with low RMSE and MAE. Water-Filled Pore Space (WFPS) at both 5 cm and 20 cm depths exhibited good correlations, with acceptable RMSE and MAE values. Similarly, for organic inputs, the DNDC model had fair correlation (ρ) for GPP (0.81), NEE (0.72), and Reco (0.85). Soil temperature and WFPS at 5 cm presented high positive correlations (ρ=0.98 and 0.55), accompanied by low RMSE and MAE. WFPS at 20cm, while exhibiting good correlation (ρ=0.065), displayed a slightly elevated RMSE and MAE. Overall, we conclude that the model offered valuable insights into GHG dynamics associated with organic and mineral fertilization practices. Overestimation of biomass yield for some of the data by DNDC suggests that future work would be well placed targeting physiology determinants of biomass in the model.

How to cite: Thentu, T., Forster, D., Virkajärvi, P., Harrison, M. T., and Shurpali, N.: Accurate simulation of greenhouse gas emissions across fertilizer scenarios with DNDC, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7642,, 2024.