Book Description

Opportunities for alternative biofuel feedstocks are widespread for a number of reasons: increased environmental and economic concerns over corn production and processing, limitations in the use of corn-based ethanol to 57 billion L (15 billion gal) by the Energy Independence and Security Act (US Congress, 2007), and target requirements of 136 billion L (36 billion gal) of renewable fuel production by 2022. The objective of this study was to select the most promising among currently available crop models that have the potential to model sweet sorghum biomass production in the central US, specifically Kansas, Oklahoma, and Texas, and to develop and test sweet sorghum crop parameters for this model. Five crop models were selected (CropSyst, CERE-Sorghum, APSIM, ALMANAC, and SORKAM), and the models were compared based on ease of use, model support, and availability of inputs and outputs from sweet sorghum biomass data and literature. After reviewing the five models, ALMANAC was selected as the best suited for the development and testing of sweet sorghum crop parameters. The results of the model comparison show that more data are needed about sweet sorghum physiological development stages and specific growth/development factors before the other models reviewed in this study can be readily used for sweet sorghum crop modeling. This study used a unique method to calibrate the sweet sorghum crop parameter development site. Ten years of crop performance data (Corn and Grain Sorghum) for Kansas Counties (Riley and Ellis) were used to select an optimum soil water (SW) estimation method (Saxton and Rawls, Ritchie et al., and a method that added 0.01 m m−1 to the minimum SW value given in the SSURGO soil database) and evapotranspiration (ET) method (Penman-Montieth, Priestley-Taylor, and Hargraeves and Samani) combination for use in the sweet sorghum parameter development. ALMANAC general parameters for corn and grain sorghum were used for the calibration/selection of the SW/ET combination. Variations in the harvest indexes were used to simulate variations in geo-climate region grain yield. A step through comparison method was utilized to select the appropriate SW/ET combination. Once the SW/ET combination was selected the combination was used to develop the sweet sorghum crop parameters. Two main conclusions can be drawn from the sweet sorghum crop parameter development study. First, the combination of Saxton and Rawls (2006) and Priestley-Taylor (1972) (SR-PT) methods has the potential for wide applicability in the US Central Plains for simulating grain yields using ALMANAC. Secondly, from the development of the sweet sorghum crop model parameters, ALMANAC modeled biomass yields with reasonable accuracy; differences from observed biomass values ranged from 0.89 to 1.76 Mg ha −1 (2.8 to 9.8%) in Kansas (Riley County), Oklahoma (Texas County), and Texas (Hale County). Future research for sweet sorghum physiology, Radiation Use Efficiency/Vapor Pressure Deficit relationships, and weather data integration would be useful in improving sweet sorghum biomass modeling.