Assessing the effects of agricultural management on nitrous oxide emissions using flux measurements and the DNDC model

Assessing Short‐Term Impacts of Management Practices on N2O Emissions From Diverse Mediterranean Agricultural Ecosystems Using a Biogeochemical Model

“…These results demonstrate that process‐based models, like DNDC, are better than the EF approach in quantifying N 2 O emissions, as well as their mitigation potentials, for cropping systems with diverse management practices. This conclusion is consistent with previous studies that also showed DNDC was more accurate than the IPCC EF methods in predicting N 2 O emissions from cropping systems (e.g., Uzoma et al, ). We note that the agreement between the simulations and observations in seasonal and annual N 2 O emissions could sometimes have resulted from compensating discrepancies between the simulated and observed daily N 2 O fluxes.…”

The development of the DNDC plant growth sub-model and the application of DNDC in agriculture: A review

“…However, with the assumption of a deep water pool located below the bottom of the modelled soil profile (50 cm) and above the drainage tiles (145 cm) to control the tile discharge flow, this soil depth (0-50 cm) should be deep enough to cover the most important biogeochemical processes while minimizing computing time . (4) DNDC will underestimate soil moisture if observed soil moisture remains above field capacity because DNDC uses a simple cascade (tipping bucket) water flow model that drains the profile to field capacity (Kröbel et al, 2010;Uzoma et al, 2015). Likewise, because DNDC does not include unsaturated flow and underestimates the rainfall loss caused by surface runoff and leaf interception, it may overestimate soil moisture at other times (Kiese et al, 2005;Tonitto et al, 2010).…”

Simulating denitrification and nitrous oxide emissions from subtropical maize-winter wheat rotations in Southwestern China using NOE v2 model

“…However, models are largely based on existing knowledge and the lack of a clear understanding of underlying mechanisms restricts their utility (Barton et al, 1999;Heinen, 2006;Chen et al, 2008). Validations with measurement data across soil, climate and land use are vital to improve model performance (Frolking et al, 1998;Li et al, 2005;Uzoma et al, 2015).…”