Energy Beets for Advanced Biofuel

Energy beets bred solely for the purpose of biofuel production are being considered as one of the most viable feedstock alternatives in the US. Preliminary results have shown that energy beets hold potential for mitigating soil salinity problem, improving the use of water and fertilizers, and complementing other crops in crop rotation. However, research in Europe has shown that using sugar beet to produce ethanol could potentially increase soil erosion and worsen downstream water quality. Through a multidisciplinary project, we will gain a better understanding of the fundamental water and solute transport processes in the root zone of energy beet fields and be able to address the sustainability issues of growing energy beets in the north-central US. In the proposed project, we will i) conduct field research to assess the impact of growing energy beet on soil health in terms of mitigating soil salinity and improving the yields of other crops in rotation, ii) improve the Root Zone Water Quality Model and apply it to simulate crop yield, water flow, and the fate and transport of salts and nitrogen in energy beet fields, iii) develop a GIS-based linear programming economic model to predict land use change surrounding an energy-beet ethanol plant, and iv) apply the Soil and Water Assessment Tool models which will incorporate the predicted land use change information to simulate the downstream water quality impact caused by ethanol production. The extension of the field-scale research results to the landscape-scale is achieved through a novel sandwich-like biogeochemical-economic-environmental modeling framework.

Modeling Growth, Development and Yield for Sugarbeet Using DSSAT. Sugarbeet is considered as one of the most viable feedstock alternatives to corn for biofuel production after 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 are restricted to local regions. The Decision Support System for Agrotechnology Transfer (DSSAT) provides a common framework for a cropping system study and currently has plant growth modules for more than 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 for 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, USA (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 (d-statistic = 0.783-0.993, rRMSE = 0.127-1.014). Although uncertainty analysis revealed that the calibrated CSM-CERES-Beet consistently over-predicted leaf numbers with false confidence, the model was successfully applied to simulate the yields for six different sugarbeet cultivars grown in North Dakota, USA in 2014-2016. CSM-CERES-Beet may be applied to simulate sugarbeet production for different soils under different management scenarios and climatic conditions in the Red River Valley and other regions.

Analysis of Parameter Sensitivity and Identifiability of Root Zone Water Quality Model (RZWQM) for Dryland Sugarbeet Modeling. 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 simulates 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 reasonably well in both 2014 (d-statistic: 0.709-0.992; rRMSE: 0.066-1.211) and 2015 (d-statistic: 0.733-0.990; rRMSE: 0.043-0.930) in terms of simulating leaf area index, top weight, root weight, soil water content, and soil nitrates. Under dry conditions, the most sensitive soil parameters were soil bulk densities and saturated hydraulic conductivities in different layers. Identifiability analysis also shows that 3-5 model parameters may be identifiable by calibration datasets. RZWQM enhanced with a sugarbeet module and its parameter analysis can be used for water use optimization under dryland conditions.

Outputs

• Sugarbeet model to be included in Decision Support System for Agrotechnology Transfer (DSSAT)

• Sugarbeet model included in Root Zone Water Quality Model 2 (RZWQM2) version 4.0