Dzotsi, K. A., B. Basso, and J. W. Jones. 2013. Development, uncertainty and sensitivity analysis of the simple SALUS crop model in DSSAT. Ecological Modelling 260:62-76.

Citable PDF link: https://lter.kbs.msu.edu/pub/3267

Simplified approaches to modeling crop growth and development have recently received more attention due to increased interest in applying crop models at large scales for various agricultural assessments. In this study, we integrated the simple version of SALUS (System Approach to Land Use Sustainability) crop model in the widely-used Decision Support System for Agrotechnology Transfer (DSSAT) to enhance the capability of DSSAT to simulate additional crops without requiring detailed parameterization. An uncertainty and sensitivity analysis was conducted using the integrated DSSAT-simple SALUS model to assess the variability in model outputs and crop parameter ranking in response to uncertainties associated with crop parameters required by the model. The influence of year, production level, and location on the effect of crop parameter uncertainty was also investigated.

Parameter uncertainty resulted in a high variability in modeled outputs. Simulated potential aboveground biomass ranged from 1.2 t ha−1 to 38 t ha−1 for maize and 4 t ha−1 to 26.5 t ha−1 for peanut and cotton, all locations and years considered. The degree of variability was dependent upon the production level, the location, the year, and the crop. Ranking of crop parameters was not significantly affected by the year of study but was strongly related to the production level, location, and crop. The model was not sensitive to parameters related to prediction of the timing of germination and emergence. The most influential parameters were related to leaf area index growth, crop duration, and thermal time accumulation. Findings from this study contributed to understanding the effects of crop parameter uncertainty on the model’s outputs under different environmental conditions.

DOI: 10.1016/j.ecolmodel.2013.03.017

Associated Treatment Areas:

Modeling

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