Offered Papers Theme C: Delivering the Benefits from Grassland
Description
The objective of this research was to modify an existing crop growth model for ability to predict growth and composition of bahiagrass (Paspalm notatum Flügge) in response to daily weather and management inputs. The CROPGRO-CSM cropping systems model has a generic, process-oriented structure that allows inclusion of new species and simulating cropping sequences and crop rotations. An early adaptation of CROPGRO-CSM "species files" for bahiagrass over-predicted growth during late fall through early spring, and totally failed in re-growth if all foliage was lost from freeze damage. Revised species parameters and use of "pest damage" offered only a partial solution. Three processes, absent from the annual CROPGRO-CSM model, contributed to prediction of excessive cool-season growth: (1) no provision for storage (reserve) structures, (2) lack of winter dormancy, and 3) freeze damage killed all leaves at once and resulted in crop death. In addition, the model lacked the CO2-concentrating effect of C4 photosynthesis in the leaf photosynthesis routines. Therefore, we modified the source code of CROPGRO to include these processes to improve biological accuracy of re-growth patterns and prediction of seasonal patterns of growth (Rymph et al., 2004).
Citation
Rymph, S. J.; Boote, K. J.; and Jones, J. W., "Adapting the CROPGRO Model to Predict Growth and Perennial Nature of Bahiagrass" (2023). IGC Proceedings (1993-2023). 82.
https://uknowledge.uky.edu/igc/20/themeC/82
Included in
Agricultural Science Commons, Agronomy and Crop Sciences Commons, Plant Biology Commons, Plant Pathology Commons, Soil Science Commons, Weed Science Commons
Adapting the CROPGRO Model to Predict Growth and Perennial Nature of Bahiagrass
The objective of this research was to modify an existing crop growth model for ability to predict growth and composition of bahiagrass (Paspalm notatum Flügge) in response to daily weather and management inputs. The CROPGRO-CSM cropping systems model has a generic, process-oriented structure that allows inclusion of new species and simulating cropping sequences and crop rotations. An early adaptation of CROPGRO-CSM "species files" for bahiagrass over-predicted growth during late fall through early spring, and totally failed in re-growth if all foliage was lost from freeze damage. Revised species parameters and use of "pest damage" offered only a partial solution. Three processes, absent from the annual CROPGRO-CSM model, contributed to prediction of excessive cool-season growth: (1) no provision for storage (reserve) structures, (2) lack of winter dormancy, and 3) freeze damage killed all leaves at once and resulted in crop death. In addition, the model lacked the CO2-concentrating effect of C4 photosynthesis in the leaf photosynthesis routines. Therefore, we modified the source code of CROPGRO to include these processes to improve biological accuracy of re-growth patterns and prediction of seasonal patterns of growth (Rymph et al., 2004).
