Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/20/2020
Publication Date: 8/6/2020
Publication URL: https://handle.nal.usda.gov/10113/7081662
Citation: Thorp, K.R., DeJonge, K.C., Marek, G.W., Evett, S.R. 2020. Comparison of evapotranspiration methods in the DSSAT Cropping System Model: I. Global sensitivity analysis. Computers and Electronics in Agriculture. 177. Article 105679. https://doi.org/10.1016/j.compag.2020.105679.
Interpretive Summary: Simulation models of agricultural field processes often require many types of data to specify the model's input parameters. Some parameters are more influential on the model output calculations than others. Sensitivity analysis is a method for evaluating how model outputs are sensitive to model input parameters. In this study, a sensitivity analysis was used to evaluate the DSSAT Cropping System Model for cotton production at a semi-arid field site in Texas. The analysis revealed that nearly half of the model input parameters were not influential on any output data and identified less than 20 parameters that were most influential on the model outputs. Large differences in sensitivity were demonstrated when choosing between two different methods for simulating soil water evaporation. The results will facilitate future scientific efforts with this model by assisting modelers with model calibration efforts. Furthermore, the results will guide model developers toward simplified parameterization requirements for this model. In particular, the study revealed several considerations for improving model implementation to address water management issues in water-limited agricultural environments where irrigation is common.
Technical Abstract: Global sensitivity analysis (GSA) is useful for evaluating the responsiveness of agroecosystem models to input parameter adjustments. Evaluations of model sensitivity for diverse water status conditions and evapotranspiration (ET) algorithms will facilitate better use of models to provide water management recommendations. The objective of this study was to conduct a GSA to identify influential parameters in the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM), specifically using the CROPGRO-Cotton module with data from cotton field studies conducted in 2000, 2001, and 2008 at Bushland, Texas. The field studies tested fully-irrigated, deficit-irrigated, and dryland cotton production in a semi-arid environment. Using high performance computing resources, a GSA was conducted to evaluate the sensitivity of 37 model input parameters with respect to 24 model outputs. The GSA was conducted for six different ET methodologies available in the model. With first-order sensitivity indices <0.05, nearly half of the tested input parameters did not influence any model output for any of the tested simulation scenarios. Among the parameters with first-order sensitivity indices >0.05, eleven were cultivar parameters that controlled crop development and growth, and five were soil parameters that specified initial soil water conditions, soil water limits, drainage rate, and root growth characteristics. The influences of another soil parameter and one ET parameter were relevant only for the ET methods that required them. Large differences in sensitivity indices were found based on the choice between two soil water evaporation methods. In addition to providing insights for other applications of this model, the results specifically informed further efforts to evaluate the model using measured data from the Bushland cotton studies to compare performance among the six ET methods, as reported in a companion paper.