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TitleA simulation model linking crop growth and soil biogeochemistry for sustainable agriculture
 
AuthorZhang, YORCID logo; Li, C; Zhou, X; Moore, B, III
SourceEcological Modelling vol. 151, no. 1, 2002 p. 75-108, https://doi.org/10.1016/S0304-3800(01)00527-0
Year2002
Alt SeriesNatural Resources Canada, Contribution Series 20181856
PublisherElsevier BV
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; Nature and Environment; remote sensing
ProgramCanada Centre for Remote Sensing Divsion
AbstractPredicting impacts of climate change or alternative management on both food production and environment safety in agroecosystems is drawing great attention in the scientific community. Most of the existing agroecosystem models emphasize either crop growth or soil processes. This paper reports the latest development of an agroecosystem model (Crop-DNDC) by integrating detailed crop growth algorithms with an existing soil biogeochemical model, DNDC (Li et al., J. Geophys. Res. (1992) 9759). In the Crop-DNDC model, crop growth is simulated not only by tracking crop physiological processes (phenology, leaf area index, photosynthesis, respiration, assimilate allocation, rooting processes and nitrogen uptake), but also by calculating water stress and nitrogen stress, which were closely related to soil biogeochemical processes and hydraulic dynamics. Crop-DNDC also quantifies crop residue incorporated in the soil at the end of each growing season. Thus the model has tightly coupled crop growth algorithms with soil biogeochemical components, and simulates carbon, nitrogen and water cycles in agroecosystems with a relatively complete scope. The model was validated against field measurements, including soil moisture, leaf area index, crop biomass and nitrogen content, and the modeled results were in agreement with observations on soil carbon dynamics and trace gas emissions as well. Sensitivity tests demonstrated that the modeled results in crop yield, soil carbon dynamics and trace gas emissions were sensitive to climate conditions, atmospheric CO2 concentration and various farming practices. There are potentials of applying the model for simultaneously predicting effects of changes in climate or management on crop yield, soil carbon sequestration and trace gas emissions.
GEOSCAN ID312211

 
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