Parameterization of the Almanac Model to Evaluate Novel High Biomass Crops On Maui Island, Hawaii.

The success of the bioenergy industry will partly depend on the availability of highly productive feedstock crops. To this end, researchers are evaluating novel high biomass and oilseed crops such as energy sorghum, energy cane, banagrass, Jatropha  etc. However, production of these crops across different landscapes face many challenges, among which is the lack of guidelines on best management practices (BMPs). Furthermore, these crops have distinct growth habits but yet poorly understood crop traits or parameters. Lack of this knowledge could impede large scale production as it poses socioeconomic and environmental risks. Crop simulation models such as ALMANAC can complement and extend the applicability of information collected in field trials, and when combined with the appropriate climate, soils, crops, and management databases, it can be applied effectively to evaluate site-specific crop productivity, BMPs, and potential environmental impacts. In this study, which is part of a joint ONR/USDA project plan for a resource assessment of feedstock supply to produce advanced biofuels in Hawaii, we are compiling key crop parameters required by the ALMANAC model to evaluate high biomass and oilseed feedstock production systems for energy sorghum, energy cane, banagrass, Jatropha, and sugarcane. The project is based on the Hawaiian Commercial & Sugar Company (HC&S) sugarcane plantation on Maui, with parallel measurements at Temple, TX. The key crop parameters are; radiation use efficiency (RUE), harvest index (HI), leaf area index (LAI), light extinction coefficient (K_c), optimal and minimum temperature for plant growth, potential heat units (PHUs), planting population, maximum crop height and rooting depth, N,P&K uptake, and level of tolerance to stresses; pests, water, N&P, Al toxicity, salinity, temperature, and soil aeration. Gathered crop parameters will be used to calibrate the ALMANAC model before rigorous testing and model validation with actual feedstock production data, and appropriate climate and soils databases.