Modeling growth, development and yield of Sugarbeet using DSSAT

Mohammad J. Anar, Zhulu Lin, Gerrit Hoogenboom, Vakhtang Shelia, William D. Batchelor, Jasper M. Teboh, Michael Ostlie, Blaine G. Schatz and Mohamed Khan

Agricultural Systems, 2019, vol. 169, issue C, 58-70

Abstract: Sugarbeet (Beta vulgaris) is considered as one of the most viable feedstock alternatives to maize for biofuel production since herbicide resistant sugarbeet was deregulated by the United States Department of Agriculture in 2012. So far, only a few sugarbeet simulation models have been developed and these models are limited to application for local regions. The Decision Support System for Agrotechnology Transfer (DSSAT) provides a common framework for a cropping system study and currently has crop modules for >40 crops. However, DSSAT currently does not include a model for sugarbeet. In this study, the Crop and Environment REsource Synthesis (CERES) Beet model was modified and incorporated into the current version of the Cropping System Model (CSM) to simulate growth, development, and yield of sugarbeet. The PEST optimizer was used for parameter estimation, transferability evaluation, and predictive uncertainty analysis. The sugarbeet model was evaluated with two sets of experimental data collected in two different regions and under different environmental conditions; one in Romania (Southeastern Europe) during 1997–1998 and the other in North Dakota, US (North America) during 2014–2016. After model calibration for specific cultivars, the CSM-CERES-Beet model performed well for the simulation of leaf area index, leaf number, leaf or top weight, and root weight for both datasets (NSE = 0.144–0.976, rRMSE = 0.127–1.014). Uncertainty analysis revealed that the calibrated CSM-CERES-Beet consistently over-predicted leaf number with false confidence, although measured leaf number also showed a significant variability. The model was successfully applied for predicting yield for six different sugarbeet cultivars grown in North Dakota during the 2014 to 2016 growing seasons. CSM-CERES-Beet could be applied for predicting sugarbeet yield for different soil and climatic conditions and various management scenarios for the Red River Valley in the US and other regions with environmental conditions favorable for sugarbeet production.

Keywords: Biofuel, Bioenergy; Crop and Environment REsource Synthesis (CERES); Cropping System Model; Decision Support System for Agrotechnology Transfer (DSSAT); Parameter Estimation (PEST) (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:169:y:2019:i:c:p:58-70

DOI: 10.1016/j.agsy.2018.11.010

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