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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Rangeland Resources & Systems Research » Research » Publications at this Location » Publication #338827

Research Project: Modeling Soil and Soil-plant Interaction Responses to Wind and Extreme Precipitation and Temperature Events under Different Management Strategies

Location: Rangeland Resources & Systems Research

Title: Analysis of parameter sensitivity and identifiability of root zone water quality model (RZWQM) for dryland sugerbeet modeling

Author
item Anar, M - North Dakota State University
item Lin, Z - North Dakota State University
item Ma, Liwang
item Bartling, Patricia - Pat
item Toboh, J - North Dakota State University
item Ostlie, M - North Dakota State University

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/22/2017
Publication Date: 12/1/2017
Citation: Anar, M.J., Lin, Z., Ma, L., Bartling, P.N., Toboh, J., Ostlie, M. 2017. Analysis of parameter sensitivity and identifiability of root zone water quality model (RZWQM) for dryland sugerbeet modeling. Transactions of the ASABE. 60:1995-2010.
DOI: https://doi.org/10.103031/trans.12313

Interpretive Summary: Sugarbeet is being considered as an alternative to corn for biofuel production since herbicide resistant energy beets were deregulated by USDA in 2012. Growing sugarbeets for biofuel production may have significant impacts on soil health and water quality in the north-central regions of the US where 50% of the nation’s total sugarbeets were produced in 2015. Almost all the current sugarbeet models simulate only plant growth and yield, but cannot simulate the effects of sugarbeet production on soil and water quality. In this study, a new sugarbeet module was developed in the Root Zone Water Quality Model (RZWQM) from the Crop and Environment REsource Synthesis (CERES) model. The Beet module was then evaluated against dryland sugarbeet production at the Carrington Research and Extension Station (North Dakota) in 2014 and 2015. The new model well simulated leaf area index, top weight, root weight, soil water content, and soil profile nitrates. Under dry conditions, the most sensitive parameters were soil bulk densities and saturated hydraulic conductivities in different soil layers. Identifiability analysis also showed that 3-5 model parameters might be identifiable by calibration datasets. The RZWQM model enhanced with a sugarbeet module and its parameter analysis can be used for water use optimization when growing sugarbeet under dryland and irrigated conditions.

Technical Abstract: Sugarbeet is being considered as one of the most viable feedstock alternatives to corn for biofuel production since herbicide resistant energy beets were deregulated by USDA in 2012. Growing sugarbeets for biofuel production may have significant impacts on soil health and water quality in the north-central regions of the US where 50% of the nation’s total sugarbeets were produced in 2015. Almost all the current sugarbeet models simulate only plant growth and yield, but have no capability to simulate the effects of sugarbeet production on soil and water quality. The Root Zone Water Quality Model (RZWQM) is a widely used model that simulate crop yield, water flow, and transport of salts and nitrogen in crop fields. RZWQM is currently linked to 23 specific crop models in the Decision Support System for Agrotechnology Transfer (DSSAT) version 4.0, but not including a sugarbeet model. In this study, the Crop and Environment REsource Synthesis (CERES) in RZWQM was adapted for Beet simulation to model the soil and water quality impact of sugarbeet for biofuel production. The Beet model was then evaluated against dryland sugarbeet production at the Carrington Research and Extension Station (North Dakota) in 2014 and 2015. PEST (Parameter ESTimation) tool in RZWQM was used for parameter estimation and sensitivity and identifiability analysis. The model did very well in 2014 (d-statistic: 0.709-0.987; rRMSE: 0.066-0.345) and reasonably well in 2015 (d-statistic: 0.863-0.990; rRMSE: 0.043-0.735) in terms of simulating leaf area index, top weight, root weight, soil water content, and soil profile nitrates. Under dry conditions, the most sensitive parameters were soil bulk densities and saturated hydraulic conductivities in different layers, besides PHINT, a phenology parameter. Identifiability analysis also showed that 3-5 model parameters might be identifiable by calibration datasets. The RZWQM model enhanced with a sugarbeet module and its parameter analysis can be used for water use optimization when growing sugarbeet under dryland and irrigated conditions.