Analysis of spring break-up and its effects on a biomass feedstock supply chain in northern Michigan

Demand for bio-fuels is expected to increase, due to rising prices of fossil fuels and concerns over greenhouse gas emissions and energy security. The overall cost of biomass energy generation is primarily related to biomass harvesting activity, transportation, and storage. With a commercial-scale cellulosic ethanol processing facility in Kinross Township of Chippewa County, Michigan about to be built, models including a simulation model and an optimization model have been developed to provide decision support for the facility. Both models track cost, emissions and energy consumption. While the optimization model provides guidance for a long-term strategic plan, the simulation model aims to present detailed output for specified operational scenarios over an annual period. Most importantly, the simulation model considers the uncertainty of spring break-up timing, i.e., seasonal road restrictions. Spring break-up timing is important because it will impact the feasibility of harvesting activity and the time duration of transportation restrictions, which significantly changes the availability of feedstock for the processing facility. This thesis focuses on the statistical model of spring break-up used in the simulation model. Spring break-up timing depends on various factors, including temperature, road conditions and soil type, as well as individual decision making processes at the county level. The spring break-up model, based on the historical spring break-up data from 27 counties over the period of 2002-2010, starts by specifying the probability distribution of a particular county’s spring break-up start day and end day, and then relates the spring break-up timing of the other counties in the harvesting zone to the first county. In order to estimate the dependence relationship between counties, regression analyses, including standard linear regression and reduced major axis regression, are conducted. Using realizations (scenarios) of spring break-up generated by the statistical spring breakup model, the simulation model is able to probabilistically evaluate different harvesting and transportation plans to help the bio-fuel facility select the most effective strategy. For early spring break-up, which usually indicates a longer than average break-up period, more log storage is required, total cost increases, and the probability of plant closure increases. The risk of plant closure may be partially offset through increased use of rail transportation, which is not subject to spring break-up restrictions. However, rail availability and rail yard storage may then become limiting factors in the supply chain. Rail use will impact total cost, energy consumption, system-wide CO2 emissions, and the reliability of providing feedstock to the bio-fuel processing facility.

A comparison of ground cover and frequency estimation methods for post-harvest soil monitoring

The amount and type of ground cover is an important characteristic to measure when collecting soil disturbance monitoring data after a timber harvest. Estimates of ground cover and bare soil can be used for tracking changes in invasive species, plant growth and regeneration, woody debris loadings, and the risk of surface water runoff and soil erosion. A new method of assessing ground cover and soil disturbance was recently published by the U.S. Forest Service, the Forest Soil Disturbance Monitoring Protocol (FSDMP). This protocol uses the frequency of cover types in small circular (15cm) plots to compare ground surface in pre- and post-harvest condition. While both frequency and percent cover are common methods of describing vegetation, frequency has rarely been used to measure ground surface cover. In this study, three methods for assessing ground cover percent (step-point, 15cm dia. circular and 1x5m visual plot estimates) were compared to the FSDMP frequency method. Results show that the FSDMP method provides significantly higher estimates of ground surface condition for most soil cover types, except coarse wood. The three cover methods had similar estimates for most cover values. The FSDMP method also produced the highest value when bare soil estimates were used to model erosion risk. In a person-hour analysis, estimating ground cover percent in 15cm dia. plots required the least sampling time, and provided standard errors similar to the other cover estimates even at low sampling intensities (n=18). If ground cover estimates are desired in soil monitoring, then a small plot size (15cm dia. circle), or a step-point method can provide a more accurate estimate in less time than the current FSDMP method.

Isolation of plant growth promoting bacteria from torch lake sediments and their role in phytoremediation of metal contaminated soil

In this study, we isolated eight copper-resistant bacteria from Torch Lake sediment contaminated by copper mine tailings (stamp sand). Sequence analysis of gyrB and rpoD genes revealed that these organisms are closer to various Pseudomonas species. These eight bacterial isolates were also resistant to zinc, cesium, lead, arsenate and mercury. Further characterization showed that all the strains produced plant growth promoting indole-3-acetic acid (IAA), iron chelating siderophore and solubilized mineral phosphate and metals. The effect of bacterial inoculation on plant growth and copper uptake by maize (Zea mays) and sunflower (Helianthus annuus) was investigated using one of the isolates (Pseudomonas sp. TLC 6-6.5-4) with higher IAA production and phosphate and metal soubilization, which resulted in a significant increase in copper accumulation in maize and sunflower, and an increase in the total biomass of maize. Genes involved in copper resistance of Pseudomonas sp. TLC 6-6.5-4 was analyzed by transposon mutational analysis. Two copper sensitive mutants with significant reduction in copper resistance were identified: CSM1, a mutant disrupted in trp A gene (tryptophan synthase alpha subunit); CSM2, a mutant disrupted in clpA gene (ATP-dependent Clp protease). Proteomic and metabolomic analysis were performed to identify biochemical and molecular mechanisms involved in copper resistance using CSM2 due to its lower minimum inhibitory concentration compared with CSM1 and the wild type. The effect of different bacterial inoculation methods on plant growth, copper uptake and soil enzyme activities was investigated. Four different delivery methods were used including soil inoculation (before or after plant emergence), seed coating and root dipping. Soil inoculation before sowing seeds and coating seeds with PGPB led to better growth of maize, higher copper uptake and an increase in soil invertase and dehydrogenase activities. Proteomic and metabolomic analyses were performed to investigate the effect of bacterial inoculation on maize grown in normal soil and stamp sand. Our results revealed that bacterial inoculation led to environment-dependent effects on maize proteome and metabolome.