Biogeochemical models are useful tools for integrating the effects of agricultural management on GHG emissions; however, their development is often hampered by the incomplete temporal and spatial representation of measurements. Adding to the problem is that a full complement of ancillary measurements necessary to understand and validate the soil processes responsible for GHG emissions is often not available. This study presents a rare case where continuous N2O emissions, measured over seven years using a flux gradient technique, along with a robust set of ancillary measurements were used to assess the ability of the DNDC model for estimating N2O emissions under varying crop-management regimes. The analysis revealed that the model estimated soil water content more precisely in the normal and wet years (ARE 3.4%) than during the dry years (ARE 11.5%). This was attributed to the model's inability to characterize episodic preferential flow through: clay cracks. Soil mineral N across differing management regimes (ARE 2%) proved to be well estimated by DNDC. The model captured the relative differences in N2O emissions between the annual (measured: 35.5 kg N2O-N ha(-1), modeled: 30.1 kg N2O-N ha(-1)) and annual-perennial (measured: 26.6 kg N2O-N ha(-1), modeled: 21.2 kg N2O-N ha(-1)) cropping systems over the 7 year period but overestimated emissions from alfalfa production and underestimated emissions after spring applied anhydrous ammonia. Model predictions compared well with the measured total N2O emissions (ARE -11%) while Tier II comparison to measurements (ARE -75%) helped to illustrate the strengths of a mechanistic approach in characterizing the site specific drivers responsible for N2O emissions. Overall this study demonstrated the benefits of having near continuous GHG flux measurements coupled with detailed ancillary measurements towards identifying soil process interactions responsible for regulating GHG emissions. Crown Copyright (C) 2015 Published by Elsevier B.V. All rights reserved.

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

Goglio P;
2015

Abstract

Biogeochemical models are useful tools for integrating the effects of agricultural management on GHG emissions; however, their development is often hampered by the incomplete temporal and spatial representation of measurements. Adding to the problem is that a full complement of ancillary measurements necessary to understand and validate the soil processes responsible for GHG emissions is often not available. This study presents a rare case where continuous N2O emissions, measured over seven years using a flux gradient technique, along with a robust set of ancillary measurements were used to assess the ability of the DNDC model for estimating N2O emissions under varying crop-management regimes. The analysis revealed that the model estimated soil water content more precisely in the normal and wet years (ARE 3.4%) than during the dry years (ARE 11.5%). This was attributed to the model's inability to characterize episodic preferential flow through: clay cracks. Soil mineral N across differing management regimes (ARE 2%) proved to be well estimated by DNDC. The model captured the relative differences in N2O emissions between the annual (measured: 35.5 kg N2O-N ha(-1), modeled: 30.1 kg N2O-N ha(-1)) and annual-perennial (measured: 26.6 kg N2O-N ha(-1), modeled: 21.2 kg N2O-N ha(-1)) cropping systems over the 7 year period but overestimated emissions from alfalfa production and underestimated emissions after spring applied anhydrous ammonia. Model predictions compared well with the measured total N2O emissions (ARE -11%) while Tier II comparison to measurements (ARE -75%) helped to illustrate the strengths of a mechanistic approach in characterizing the site specific drivers responsible for N2O emissions. Overall this study demonstrated the benefits of having near continuous GHG flux measurements coupled with detailed ancillary measurements towards identifying soil process interactions responsible for regulating GHG emissions. Crown Copyright (C) 2015 Published by Elsevier B.V. All rights reserved.
2015
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1552680
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