High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials. © 2015, The Author(s).

Implications of retaining woody regrowth for carbon sequestration for an extensive grazing beef business: a bio-economic modelling case study

A bio-economic modelling framework (GRASP-ENTERPRISE) was used to assess the implications of retaining woody regrowth for carbon sequestration on a case study beef grazing property in northern Australia. Five carbon farming scenarios, ranging from 0% to 100% of the property regrowth retained for carbon sequestration, were simulated over a 20-year period (1993–2012). Dedicating regrowth on the property for carbon sequestration reduced pasture (up to 40%) and herd productivity (up to 20%), and resulted in financial losses (up to 24% reduction in total gross margin). A net carbon income (income after grazing management expenses are removed) of $2–4 per t CO2-e was required to offset economic losses of retaining regrowth on a moderately productive (~8 ha adult equivalent–1) property where income was from the sale of weaners. A higher opportunity cost ($ t–1 CO2-e) of retaining woody regrowth is likely for feeder steer or finishing operations, with improved cattle prices, and where the substantial transaction and reporting costs are included. Although uncertainty remains around the price received for carbon farming activities, this study demonstrated that a conservatively stocked breeding operation can achieve positive production, environmental and economic outcomes, including net carbon stock. This study was based on a beef enterprise in central Queensland’s grazing lands, however, the approach and learnings are expected to be applicable across northern Australia where regrowth is present.