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Geographic Information System (GIS) tools have been used to strategically locate bioenergy facilities and optimize the relationship between biomass supply and demand, aiming to minimize overall fuel production costs. Microalgae, also termed third generation bioenergy feedstocks, are discussed for their potential to meet future energy demands. This study reviews literature on GIS applications to locate algae cultivation sites and estimate algae biofuel potential. To highlight the diversity of results, a quantitative comparison for the US studies is presented. We found two major assumptions that primarily limited the algae biofuel production potential estimates: (1) the production technology (open pond or photobioreactor), and (2) the number and type of resources considered, such as land type, CO 2 , water source, water quality, etc. All studies used binary (a location is either unsuitable or suitable) suitability models to determine areas for algae production. Most studies considered water, land, and CO 2 resources, while some also accounted for infrastructure, soil properties, and work force requirements. We found that potential cultivation area in the USA is most sensitive to the constraints of CO 2 availability and land cost. This review explains the wide range of algal biofuel potential estimates (from 0.09 to over 600 billion L yr −1 ) by identifying underlying assumptions, methodologies, and data. The highly variable outputs indicate the need for a comprehensive analysis of different criteria individually and in combination to estimate realistic biofuel potential. The results suggest that with models becoming increasingly detailed in considering resources and conversion/production technologies, further decrease in estimated theoretical production potential is expected under available technology.

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An environmental chamber was built to evaluate the effect of weather parameters and swath density that affect the drying rate of crops during field drying. A series of 52 drying experiments were conducted on corn stover (CS) of which 27 were used for model development and 25 were used for model validation. Similarly, 80 experiments were performed on switchgrass of which 72 were used for model development and eight were used for model validation. Regression models were developed for switchgrass and CS that predicted the drying rate based on environmental conditions and swath density. During the day, radiation was found to be the most significant variable that affected the drying rate of switchgrass with a correlation coefficient (r) of 0.5 and 0.49 during different maturity stages. During the night, VPD was the most significant variable that affected the drying rate with r of 0.69 for corn stover (CS) and 0.83 to 0.85 for switchgrass.The effect of wind speed was variable and was found to be dependent on solar radiation. During the day time, an increase in wind speed removed the heat produced by radiation and thus decreased the drying rate. However, at night, the wind speed was positively correlated with drying rate. Swath density was negatively correlated (r = -0.38) with the drying rate of switchgrass which suggested that biomass should be dried in wide swaths if possible. The model should be a useful tool for planning field logistics and transportation operations for biomass supply.

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During field drying, crops can be subjected to rainfall losses due to leaching, respiration and mechanical treatments. The objective of this study was to measure the impact of rainfall amount (8 to 75 mm) and crop density (0.8 to 2.6 (corn stover), 1 to 3.2 (switchgrass) kg [DM] m-2) on dry matter and composition change of corn stover (CS) and switchgrass. CS and switchgrass lost 0.3 to 4.7% and 0.2 to 2.8% as leaching loss from 8 to 75 mm of rainfall, respectively. After the incubation period of 48 h, the dry matter loss increased to 7.2 to 9.8% (CS) and 2.6 to 6.1% (switchgrass) from 8 to 75 mm of rainfall, respectively. Water soluble portion of CS and switchgrass was more severely affected than the fibre portion. Corn stover, being more exposed to rainfall in low density (LD) swaths, lost 56.7% ash content, compared to 19% in high density (HD) swaths. In CS, a significant decrease of K (10.2 to 63.8%) and Mg (5.6 to 41.7%) was observed with greater reductions in LD swaths compared to HD swaths. Similarly, a significant decrease in K (6.2 to 23.0%) and Mg (5.1 to 17%) content was observed in switchgrass but it was less prominent than CS.