Variations in seasonal snowfall regulate regional and global climatic systems and vegetation growth by changing energy budgets of the lower atmosphere and land surface. We investigated the effects of snow on the start of growing season (SGS) of temperate vegetation in China. Across the entire temperate region in China, the winter snow depth increased at a rate of 0.15 cm yr(-1) (P = 0.07) during the period 1982-1998, and decreased at a rate of 0.36 cm yr(-1) (P = 0.09) during the period 1998-2005. Correspondingly, the SGS advanced at a rate of 0.68 day yr(-1) (P < 0.01) during 1982-1998, and delayed at a rate of 2.13 day yr(-1) (P = 0.07) during 1998-2005, against a warming trend throughout the entire study period of 1982-2005. Spring air temperature strongly regulated the SGS of both deciduous broad-leaf and coniferous forests, whereas the winter snow had a greater impact on the SGS of grassland and shrubs. Snow depth variation combined with air temperature contributed to the variability in the SGS of grassland and shrubs, as snow acted as an insulator and modulated the underground thermal conditions. In addition, differences were seen between the impacts of winter snow depth and spring snow depth on the SGS; as snow depths increased, the effect associated went from delaying SGS to advancing SGS. The observed thresholds for these effects were snow depths of 6.8 cm (winter) and 4.0 cm (spring). The results of this study suggest that the response of the vegetation's SGS to seasonal snow change may be attributed to the coupling effects of air temperature and snow depth associated with the underground thermal conditions.

Biomass such as agricultural residues, energy crops and yard waste hassignificant potential to be used as renewable feedstocks for production of fuels, chemicals and energy. However, in a given location, biomass availability, cost and quality can vary markedly. Strategies to manage these traits must be identified and implemented so that consistent low-cost and high-quality feedstocks can be delivered to biorefineries year round. In this study, we examine air classification as a method to mitigate high ash concentrations in corn stover, switchgrass, and grass clippings. Formulation techniques were then used to produce blends that met ash quality and biomass quantity specifications at the lowest possible cost for biopower and biochemical conversion applications. It was found that air classification can separate the biomass into light fractions which contain concentrated amounts of elemental ash components introduced through soil contamination such as sodium, alumina, silica, iron and titania; and heavy fractions that are depleted in these components and have relatively lower total ash content. Light fractions of corn stover and grass clippings were found to be suitable for combustion applications since they had less propensity to slag than the whole biomass material. The remaining heavy fractions of corn stover orgrass clippings could then be blended with switchgrass to produce blends that met the 5% total ash specifications suggested for biochemical conversions. However, ternary blends of the three feedstocks were not possible

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The success of lignocellulosic biofuels and biochemical industries depends on an economic and reliable supply of high-quality biomass. However, research and development efforts have been historically focused on the utilization of agriculturally derived cellulosic feedstocks, without considerations of their low energy density, high variations in compositions and potential supply risks in terms of availability and affordability. This chapter demonstrated a strategy of feedstock blending and densification to address the supply chain challenges. Blending takes advantage of low-cost feedstock to avoid the prohibitive costs incurred through reliance on a single feedstock resource, while densification produces feedstocks with increased bulk density and desirable feed handling properties, as well as reduced transportation cost. We also review recent research on the blending and densification dealing with various types of feedstocks with a focus on the impacts of these preprocessing steps on biochemical conversion, that is, various thermochemical pretreatment chemistries and enzymatic hydrolysis, into fermentable sugars for biofuel production.

Feedstock blending can enable nationwide production of biofuels. Predictive model can identify ideal blend ratios to achieve high sugar yields. A low ratio of high-quality feedstock can substantially improve sugar yields. g r a p h i c a l a b s t r a c t a r t i c l e i n f o b s t r a c tCommercial-scale bio-refineries are designed to process 2000 tons/day of single lignocellulosic biomass. Several geographical areas in the United States generate diverse feedstocks that, when combined, can be substantial for bio-based manufacturing. Blending multiple feedstocks is a strategy being investigated to expand bio-based manufacturing outside Corn Belt. In this study, we developed a model to predict continuous envelopes of biomass blends that are optimal for a given pretreatment condition to achieve a predetermined sugar yield or vice versa. For example, our model predicted more than 60% glucose yield can be achieved by treating an equal part blend of energy cane, corn stover, and switchgrass with alkali pretreatment at 120°C for 14.8 h. By using ionic liquid to pretreat an equal part blend of the biomass feedstocks at 160°C for 2.2 h, we achieved 87.6% glucose yield. Such a predictive model can potentially overcome dependence on a single feedstock.