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DNDC is a well-established biogeochemical crop model that has been featured in over 400 peer-reviewed publications and excels at quantifying agricultural impact under diverse environmental and management conditions. DNDC employs a thermal time-driven biomass accumulation sub-model across all crops with differences arising from regionspecific parameters which serve as proxies for genetic variation and from simulation approaches tailored to crop physiology and environment. For instance, DNDC simulates the nitrogen fixation of legumes and nitrous oxide emissions from flooded rice fields. Deriving these parameters for new locations requires calibration data, typically sourced from site-specific literature. This chapter outlines DNDC’s history and functionality and introduces a methodology that uses aggregated yield data to generate local crop parameters. A case study in the US Corn Belt compares simulated yield results from parameters calibrated with different approaches. The chapter highlights that less data- intensive calibration methodologies are critical for maximizing the benefits of models like DNDC.
- 1 Introduction
- 2 The history of DNDC
- 3 The crop growth model in DNDC
- 4 Use cases and running DNDC
- 5 Spatial calibration approach
- 6 Spatial calibration results
- 7 Spatial calibration: conclusions
- 8 Conclusion
- 9 Where to look for further information
- 11 Appendix 2: DNDC v9.5 defaults
- 12 References