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Title:Miscanthus X Giganteus Production: Meta-Analysis, Field Study and Mathematical Modeling
Author(s):Miguez, Fernando E.
Doctoral Committee Chair(s):German Bollero
Department / Program:Crop Sciences
Discipline:Crop Sciences
Degree Granting Institution:University of Illinois at Urbana-Champaign
Abstract:This is a comprehensive study of the potential of M. x giganteus as a biomass crop for energy production. The study looks at the past, present and future of M. x giganteus for its potential for dry biomass production. In Chapter 1 the published M. x giganteus literature, including 31 European studies, was analyzed using non-linear mixed models. This quantitative review of the literature (i.e. meta-analysis) revealed patterns across and within growing seasons. This analysis not only revealed the effects of the growing season, planting density and nitrogen (N) fertilizer but also provided a measure of uncertainty about these effects. More importantly, it provided statistical models which are essentially testable hypotheses. These models were used in Chapter 2 to predict dry biomass considering effects of growing season, planting density and N fertilizer in Illinois. In Chapter 2 M. x giganteus and P. virgatum were investigated in a field study at two locations at Savoy and Perry, IL in 2006. This field study evaluated the performance of M. x giganteus and P. virgatum with respect to their response to N fertilizer and hairy vetch (a winter legume). Variables analyzed included light interception, greenness and dry biomass. In Chapter 3 a mechanistic model, WIMOVAC, was descried and parameterized for simulating M. x giganteus production. The model showed great potential at predicting photosynthesis, leaf area index and dry biomass partitioning in M. x giganteus European experiments. HE Chapter 4, the model described in Chapter 3 was re-implemented and improved, termed BIOCRO with the objective of rigorously estimating key parameters related to dry biomass partitioning. The Bayesian statistical framework for parameter estimation was achieved through the use of Markey chain Monte Carlo methods. The algorithm proposed can be used to estimate parameters in large complex models as the one proposed here. The future of this research points to novel ways in which current understanding of plant physiology and genetics can be integrated to improve dry biomass production of biomass crops and thus realizing a bio-energy fueled society.
Issue Date:2007
Description:186 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.
Other Identifier(s):(MiAaPQ)AAI3301195
Date Available in IDEALS:2015-09-25
Date Deposited:2007

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