Uncertainty Analysis and Parameter Estimation for the CSM-CROPGRO-Cotton Model

Published in Agron. J. 104:1363–1373 (2012) Posted online 10 July 2012 doi:10.2134/agronj2011.0349 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. A of crop simulation models are important in agriculture. Crop modelers are often interested in studying the impacts of agricultural management and climate scenarios on crop growth and development and in identifying practices to enhance crop yield. Such studies typically involve the use of crop simulation models that are calibrated and evaluated for field conditions. In addition, estimating and incorporating uncertainties associated with model parameters and the resulting uncertainties in model predictions for such model applications has also become increasingly important. Uncertainties in simulation models can be attributed to three major sources: model parameters, model input data, and model structure. Because experimental data sets are small, estimates of model parameters are uncertain. When estimating parameters for a model, structural uncertainty is set to 0 and is defined by the computer code used to construct the model; however, the simplified representation of reality inherent in any model structure may lead to a bias in model outputs. Lastly, model input data contribute toward output uncertainty due to measurement error (Makowski et al., 2006; Tolson and Shoemaker, 2008). Model parameters have been identified as a significant source of uncertainty in model prediction in previous studies. Brazier et al. (2000) showed that parameters such as hydraulic conductivity are very variable within a single soil type (Nielsen et al., 1973; Warrick and Nielsen, 1980), yet only a single value is used for model predictions. The same applies to other soil inputs, such as water retention parameters used by the water balance routine in the Decision Support System for Agrotechnology Transfer (DSSAT)-CSM. If model output is sensitive to hydraulic conductivity, the major portion of model output uncertainty could be caused by parameter uncertainty. Wang et al. (2005) performed parameter estimation and uncertainty analysis on crop yield and soil organic C simulated with the EPIC model, considering only parameter uncertainty, using GLUE. They showed that the observed corn (Zea mays L.) yield and soil organic C fell within the 95% confidence limit of the model predictions and GLUE improved the model performance. There have been significant advances in methodologies for uncertainty assessment. Blasone et al. (2008) referenced some of the widely used uncertainty analysis methods that include classical Bayesian (Vrugt et al., 2003; Thiemann et al., 2001), pseudo-Bayesian (Beven and Binley, 1992), data assimilation (Moradkhani et al., 2005), and multimodel averaging methods (Georgakakos et al., 2004; Ajami et al., 2007). These methods differ in their underlying assumptions, complexity, and the way different sources of error are treated. Montanari (2007) suggested that the selection of an uncertainty analysis method is subjective and should take into account issues such as model complexity, the type of observed data set availability, and the reliability of uncertainty ABSTRACT The Cropping System Model (CSM)-CROPGRO-Cotton model simulates growth, development, and yield for cotton (Gossypium hirsutum L.) and requires a large number of parameters and inputs. It is practically impossible to estimate all the parameters with a high level of accuracy. The objectives of this study were to estimate values and uncertainties of the CSM-CROPGRO-Cotton model genotype parameters and resulting uncertainties in the cotton growth and development model and to evaluate the model performance by comparing model predictions based on the estimated parameters using generalized likelihood uncertainty estimation (GLUE) with those based on a prior distribution and the default model parameters. Observations for this study were collected from four experiments at Quincy, FL, Citra, FL, and Griffin, GA, for Delta Pine 555 cotton. The results show that the estimated parameters improved model performance compared with default parameters. There was a noticeable reduction in parameter uncertainties and resulting model output uncertainties. Resulting uncertainty in model predictions of leaf area index, leaf weight, stem weight, and boll weight were reduced to 4 to 13% with posterior and 29 to 56% prior parameters, partially due to improved estimates and a considerable reduction in the uncertainty of the important model parameters such as the light extinction coefficient and specific leaf area. The GLUE technique improved the model performance by improving estimated values of model parameters, reducing model parameter uncertainties, and reducing model output uncertainties.