Abstract:The bulk density of raw corn stover is a major limitation to its large-scale viability as a biomass feedstock. Raw corn stover has a bulk density of 50 kg/m 3 , which creates significant transportation costs and limits the optimization of transport logistics. Producing a densified corn stover product during harvest would reduce harvest and transportation costs, resulting in viable pathways for the use of corn stover as a biomass feedstock. This research investigated the effect of different process variables (compression pressure, moisture content, particle size, and material composition) on a densification method that produces briquettes from raw corn stover. A customized bench-scale densification system was designed to evaluate different corn stover inputs. Quality briquette production was possible using non-reduced particle sizes and low compression pressures achievable in a continuous in-field production system. At optimized bench settings, corn stover was densified to a dry bulk density of 190 kg/m 3 . Corn stover with a moisture content above 25% wb was not suitable for this method of bulk densification, and greater cob content had a positive effect on product quality.
Conservation and land management decisions often are based primarily on natural science, but could be more successful if human influences were effectively integrated into decision making. This is especially true for efforts to manage invasive plants, whose arrival is usually the product of deliberate human introduction. Risk-assessment models that predict the probability that a nonnative plant will naturalize or invade are useful tools for managing invasive plants. However, decisions based on such models could affect stakeholders differently. Careful assessment of risk-analysis methodologies should consider the importance of stakeholder participation. We surveyed the perceptions of four stakeholder groups (conservation professionals, master gardeners, professional horticulturists, and woodland landowners) in Iowa about invasive plants, general management approaches, and risk-assessment models. We also examined whether or not a stakeholder's nature relatedness plays a role in shaping his or her responses. Stakeholder perceptions varied less than expected across all four groups. Eighty-seven percent of respondents agreed invasive plants are a problem, and 88.4% agreed that we have a responsibility to manage them to protect natural areas. Support for the use of risk-assessment models also was high, with 78.7% of respondents agreeing that their use has potential to prevent plant invasions. Nature relatedness scores for all groups were correlated with respondent perspectives on invasive plants. Respondents believed biologically significant error rates (errors that might introduce a new invasive plant) should not exceed 5 to 10%. Respondents were more tolerant of horticulturally limiting errors (errors that restrict sale/use of a plant that would not have become invasive), reporting rates of 10 to 20% as acceptable. Researchers developing risk-assessment models might wish to aim for error rates within these bounds. General agreement among these stakeholder groups suggests potential support for future risk-management efforts related to invasive plants.
Accurate methods to predict the naturalization of non-native woody plants are key components of risk-management programs being considered by nursery and landscape professionals. The objective of this study was to evaluate four decision-tree models to predict naturalization (first tested in Iowa) on two new sets of data for non-native woody plants cultivated in the Chicago region. We identified life-history traits and native ranges for 193 species (52 known to naturalize and 141 not known to naturalize) in two study areas within the Chicago region. We used these datasets to test four models (one continental-scale and three regional-scale) as a form of external validation. Application of the continental-scale model resulted in classification rates of 72–76%, horticulturally limiting (false positive) error rates of 20–24%, and biologically significant (false negative) error rates of 5–6%. Two regional modifications to the continental model gave increased classification rates (85–93%) and generally lower horticulturally limiting error rates (16–22%), but similar biologically significant error rates (5–8%). A simpler method, the CART model developed from the Iowa data, resulted in lower classification rates (70–72%) and higher biologically significant error rates (8–10%), but, to its credit, it also had much lower horticulturally limiting error rates (5–10%). A combination of models to capture both high classification rates and low error rates will likely be the most effective until improved protocols based on multiple regional datasets can be developed and validated.
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