Learn more about the Crop-DNDC project

About Crop-DNDC

Crop-DNDC is a process-orientated, agroecosystem model that integrated detailed crop growth algorithms with the DNDC soil biogeochemical model to better simulate carbon, nitrogen and water cycles.  Crop-DNDC was developed at the Canada Center for Remote Sensing, Ottawa, and simulates crop growth through tracking physiological processes (such as phenology, leaf area index, photosynthesis, respiration, assimilate allocation, rooting processes and nitrogen uptake) along with water stress and nitrogen stress. Thus, the model, which operates primarily on a daily time-step can simultaneously predict crop growth and soil biochemical dynamics.  The original DNDC model was developed to simulate nitrous oxide (N2O) emissions from annual cropping systems in the United States (Li et al., 1992).

Zhang et al. (2002) describe the model as consisting of three interacting submodels: a climatic submodel calculates water dynamics and soil temperature profile; a crop submodel simulated crop phenological development, photosynthesis, respiration, leaf area index, assimilate allocation, rooting processes and nitrogen uptake; and a soil biogeochemistry submodel predicts decomposition, nitrification, denitrification and trace gas emissions.  The soil profile is divided into multiple layers, where analysis is conducted layer by layer, and crop residue is incorporated into the soil at the end of each growing season.  Further information on the submodels can be found in Zhang et al. (2002).  Required input data includes climate drivers, soil features, crop parameters and farming practices.  The output includes crop production, soil carbon and nitrogen pools and fluxes, nitrate leaching and trace gas emissions.

Validation was focused on the newly developed parts of the Crop-DNDC model using 4 crop experiments (3 from China and 1 from the US) which included field measurements of soil moisture, leaf area index, crop biomass and nitrogen content. The Crop-DNDC model results were shown to capture the patterns of soil moisture, crop growth and soil carbon and nitrogen dynamics (Zhang et al., 2002).  In addition, sensitivity analysis by Zhang et al. (2002) revealed that modelled results in crop yield, soil carbon dynamics and trace gas emissions were sensitive to climate conditions, atmospheric carbon dioxide (CO2) concentration and various farming practises. Since the model is able to simulate crop yields, soil carbon sequestration and trace gas emissions it can potentially be used for predicting the impacts of alternative management strategies or climate change on agricultural production and environmental safety (Zhang et al., 2002).

Much of the functionality in Crop-DNDC has been incorporated into the latest version of DNDC (DNDC9.5, 2011). 

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