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Comparison of RZWQM2 and DNDC model in simulating greenhouse gas emission, crop yield and subsurface drainage
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2018 ASABE Annual International Meeting 1801588.(doi:10.13031/aim.201801588)
Authors: Qianjing Jiang, Zhiming Qi, Chandra A Madramootoo, Ward Smith, Naeem A. Abbasi, Tie-Quan Zhang
Keywords: controlled drainage; inorganic fertilization; manure application; subirrigation; water table management
Abstract. Process-based models are promising tools to investigate the potential impact of agronomic management and climate change on greenhouse gas emissions. In this study, the newly developed subirrigation module and greenhouse gas emission component of RZWQM2 (Root Zone Water Quality Model) were tested and then compared with the DNDC (DeNitrification–DeComposition) model using the measured data from a subsurface drained and irrigated field with a corn-soybean rotation cropping system in Harrow, Ontario. Field measured data included the N2O and CO2 flux, soil temperature, soil moisture content, drainage and crop yield from the four-year field experiment (2012 -2015) under four treatments: inorganic fertilization under free drainage (DR-IF), inorganic fertilization under controlled drainage with subirrigation (CDS-IF), solid cattle manure under free drainage (DR-SCM), solid cattle manure under controlled drainage with sub-irrigation (CDS-SCM). The RZWQM2 was evaluated for all the four treatments while the DNDC model was only evaluated under two treatments with free drainage due to its unavailability of controlled drainage and sub-irrigation component. Both models well estimated the soil temperature, but RZWQM2 performed better than DNDC model in simulating the soil water content (SWC) due to the better applicability of Richards‘ equation than the cascade equation. Simulation of CO2 emission by both RZWQM2 and DNDC model agreed well with total measured values, but the RZWQM2 simulated CO2 was in better agreement with the measured values than DNDC model with both IF and SCM under DR system. Both two models were effective in predicting the grain yield of corn and soybean with PBIAS within 15% and 20% for RZWQM2 and DNDC model, respectively. Overall, although RZWQM2 required experienced calibration and validation work, it gave more consistent and comprehensive predictions than DNDC model.
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