Cell Wall Composition and Ruminant Digestibility of Various Maize Tissues Across Development

Hansey, Candice N.; Lorenz, Aaron J.; León, Natalia de

doi:10.1007/s12155-009-9068-4

Cited by 30 publications

(38 citation statements)

References 38 publications

(35 reference statements)

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“…The endosperm, roots and tassels also had a large number of genes with organ-specific expression, similar to that observed in rice (Wang et al, 2010). Very few organ-specific genes were observed for internodes and cobs, two organs that collectively contribute to approximately 60% of total maize stover biomass (Hansey et al, 2010) and are potential targets for biomass improvement. Thus, conventional and transgenic approaches designed to target these organs for improvements in biomass quality will probably also affect other organs.…”

Section: Identification and Expression Dynamics Of Organ-specific Genessupporting

confidence: 61%

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Genome‐wide atlas of transcription during maize development

et al. 2011

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SUMMARYMaize is an important model species and a major constituent of human and animal diets. It has also emerged as a potential feedstock and model system for bioenergy research due to recent worldwide interest in developing plant biomass-based, carbon-neutral liquid fuels. To understand how the underlying genome sequence results in specific plant phenotypes, information on the temporal and spatial transcription patterns of genes is crucial. Here we present a comprehensive atlas of global transcription profiles across developmental stages and plant organs. We used a NimbleGen microarray containing 80 301 probe sets to profile transcription patterns in 60 distinct tissues representing 11 major organ systems of inbred line B73. Of the 30 892 probe sets representing the filtered B73 gene models, 91.4% were expressed in at least one tissue. Interestingly, 44.5% of the probe sets were expressed in all tissues, indicating a substantial overlap of gene expression among plant organs. Clustering of maize tissues based on global gene expression profiles resulted in formation of groups of biologically related tissues. We utilized this dataset to examine the expression of genes that encode enzymes in the lignin biosynthetic pathway, and found that expansion of distinct gene families was accompanied by divergent, tissue-specific transcription patterns of the paralogs. This comprehensive expression atlas represents a valuable resource for gene discovery and functional characterization in maize.

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Section: Identification and Expression Dynamics Of Organ-specific Genessupporting

confidence: 61%

“…, 2010). Very few organ‐specific genes were observed for internodes and cobs, two organs that collectively contribute to approximately 60% of total maize stover biomass (Hansey et al. , 2010) and are potential targets for biomass improvement.…”

Section: Resultsmentioning

confidence: 99%

Genome‐wide atlas of transcription during maize development

et al. 2011

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“…The determination of fibres, such as neutral detergent fibre (NDF) and acid detergent fibre (ADF), was done according to the ANKOM (http://www.ankom.com/product/ ankom-2000-automated-fiber-analyzer,-120v.aspx) filter bag procedure (Hansey, Lorenz, and De Leon 2010).The moisture content for plants sampled from each plot was calculated using the wet-dry weight method. The information obtained on N, CP, moisture, and fibre was pooled for use in the statistical analysis phase.…”

Section: Chemical Analysismentioning

confidence: 99%

Predicting C3 and C4 grass nutrient variability using in situ canopy reflectance and partial least squares regression

Adjorlolo

Mutanga

Cho

2015

International Journal of Remote Sensing

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The use of hyperspectral data to estimate forage nutrient content can be a challenging task, considering the multicollinearity problem, which is often caused by high data dimensionality. We predicted some variability in the concentration of limiting nutrients such as nitrogen (N), crude protein (CP), moisture, and non-digestible fibres that constrain the intake rate of herbivores. In situ hyperspectral reflectance measurements were performed at full canopy cover for C3 and C4 grass species in a montane grassland environment. The recorded spectra were resampled to 13 selected band centres of known absorption and/or reflectance features, WorldView-2 band settings, and to 10 nm-wide bandwidths across the 400-2500 nm optical region. The predictive accuracy of the resultant wavebands was assessed using partial least squares regression (PLSR) and an accompanying variable importance (VIP) projection. The results indicated that prediction accuracies ranging from 66% to 32% of the variance in N, CP, moisture, and fibre concentrations can be achieved using the spectral-only information. The red, red-edge, and shortwave infrared (SWIR) wavelength regions were the most sensitive to all nutrient variables, with higher VIP values. Moreover, the PLSR model constructed based on spectra resampled around the 13 preselected band centres yielded the highest sensitivity to the predicted nutrient variables. The results of this study thus suggest that the use of the spectral resampling technique that uses only a few but strategically selected band centres of known absorption or reflectance features is sufficient for forage nutrient estimation.

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“…The last two steps are generally combined into a single process known as simultaneous saccharification and fermentation (SSF) (Dowe & McMillan, 2001). Evaluation of the biochemical conversion potential of biomass using the complete conversion process is not practical when processing a large number of samples due to the timeconsuming nature of the procedure and the need for specialized equipment (Isci et al, 2008;Hansey et al, 2010).…”

Section: Feedstock Characteristic Measurementsmentioning

confidence: 99%

“…An alternative, and less time consuming, method for estimating the biochemical conversion potential of biomass is using detergent fiber analysis (Van Soest, 1967), which is used routinely by forage quality laboratories. Detergent fiber analyses have been found to be highly predictive of fermentation-based biofuel yields from cellulosic biomass and have been used successfully to predict biofuel yield potential (Lorenz et al, 2009;Hansey et al, 2010). Detergent fiber analyses estimate the cell wall components of the plant, which are comprised of hemicellulose, cellulose, and lignin, with small amounts of ash and protein (Moore et al, 2007).…”

Section: Feedstock Characteristic Measurementsmentioning

confidence: 99%

Functional group and fertilization affect the composition and bioenergy yields of prairie plants

et al. 2012

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Prairies used for bioenergy production have potential to generate marketable products while enhancing environmental quality, but little is known about how prairie species composition and nutrient management affect the suitability of prairie biomass for bioenergy production. We determined how functional-group identity and nitrogen fertilization affected feedstock characteristics and estimated bioenergy yields of prairie plants, and compared those prairie characteristics to that of corn stover. We tested our objectives with a field experiment that was set up as a 5 9 2 incomplete factorial design with C 3 grasses, C 4 grasses, legumes, and multi-functionalgroup mixtures grown with and without nitrogen fertilizer; a fertilized corn treatment was also included. We determined cell wall, hemicellulose, cellulose, and ash concentrations; ethanol conversion ratios; gross caloric ratios; aboveground biomass production; ethanol yields; and energy yields for all treatments. Prairie functionalgroup identity affected the biomass feedstock characteristics, whereas nitrogen fertilization did not. Functional group and fertilization had a strong effect on aboveground biomass production, which was the major predictor of ethanol and energy yields. C 4 grasses, especially when fertilized, had among the most favorable bioenergy characteristics with high estimated ethanol conversion ratios and nongrain biomass production, relatively high gross caloric ratios, and low ash concentrations. The bioenergy characteristics of corn stover, from an annual C 4 grass, were similar to those of the biomass of perennial C 4 grasses. Both functional-group composition and nitrogen fertility management were found to be important in optimizing bioenergy production from prairies.

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Cell Wall Composition and Ruminant Digestibility of Various Maize Tissues Across Development

Cited by 30 publications

References 38 publications

Genome‐wide atlas of transcription during maize development

Genome‐wide atlas of transcription during maize development

Predicting C3 and C4 grass nutrient variability using in situ canopy reflectance and partial least squares regression

Functional group and fertilization affect the composition and bioenergy yields of prairie plants

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