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Cell Wall Composition and Bioenergy Potential of Rice Straw Tissues Are Influenced by Environment, Tissue Type, and Genotype

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Abstract

Breeding has transformed wild plant species into modern crops, increasing the allocation of their photosynthetic assimilate into grain, fiber, and other products for human use. Despite progress in increasing the harvest index, much of the biomass of crop plants is not utilized. Potential uses for the large amounts of agricultural residues that accumulate are animal fodder or bioenergy, though these may not be economically viable without additional efforts such as targeted breeding or improved processing. We characterized leaf and stem tissue from a diverse set of rice genotypes (varieties) grown in two environments (greenhouse and field) and report bioenergy-related traits across these variables. Among the 16 traits measured, cellulose, hemicelluloses, lignin, ash, total glucose, and glucose yield changed across environments, irrespective of the genotypes. Stem and leaf tissue composition differed for most traits, consistent with their unique functional contributions and suggesting that they are under separate genetic control. Plant variety had the least influence on the measured traits. High glucose yield was associated with high total glucose and hemicelluloses, but low lignin and ash content. Bioenergy yield of greenhouse-grown biomass was higher than field-grown biomass, suggesting that greenhouse studies overestimate bioenergy potential. Nevertheless, glucose yield in the greenhouse predicts glucose yield in the field (ρ = 0.85, p < 0.01) and could be used to optimize greenhouse (GH) and field breeding trials. Overall, efforts to improve cell wall composition for bioenergy require consideration of production environment, tissue type, and variety.

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Acknowledgments

We thank members of the authors’ labs for technical assistance with sample preparation and Jim ZumBrunnen from the Colorado State University Statistics Department for assistance with statistical analyses. This research was funded with support from Office of Science, Office of Biological and Environmental Research of the U.S. Department of Energy (DOE-BER) under Contract No. DE-FG02-08ER64629, International Rice Research Institute (IRRI) and U.S. Agency for International Development (USAID) Linkage grant DRPC2011-42, U.S. Department of Agriculture National Institute of Food and Agriculture (USDA-NIFA) award 2008-35504-0485, the Colorado State University Energy Institute, Department of Energy Great Lakes Bioenergy Research Center Office of Science Grant DE-FC02-07ER64494, and the Joint BioEnergy Institute supported by DOE-BER under Contract No. DE-AC02-05CH11231 and U.S. National Science Foundation (NSF), Plant Genome Research Program Grant #IOS 0701119.

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Authors and Affiliations

  1. Bioagricultural Sciences and Pest Management, Colorado State University, 1177 Campus Delivery, Fort Collins, CO, 80523-1177, USA

    Paul Tanger, Courtney E. Jahn, John K. McKay & Jan E. Leach

  2. Lawrence Berkeley National Laboratory, Physical Biosciences Division, Berkeley, CA, USA

    Miguel E. Vega-Sánchez, James B. Abrahamson & Pamela C. Ronald

  3. Department of Biology, Colorado State University, Fort Collins, CO, USA

    Margaret Fleming & Daniel R. Bush

  4. Sandia National Laboratory, Livermore, CA, USA

    Kim Tran, Seema Singh & Blake A. Simmons

  5. Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI, USA

    Nicholas Santoro

  6. International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines

    Elizabeth B. Naredo, Marietta Baraoidan, Kenneth L. McNally & Hei Leung

  7. Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, Scotland, UK

    John M. C. Danku & David E. Salt

  8. Department of Plant Pathology and the Genome Center, University of California, Davis, CA, USA

    Pamela C. Ronald

  9. Joint BioEnergy Institute, Emeryville, CA, USA

    Miguel E. Vega-Sánchez, Kim Tran, Seema Singh, James B. Abrahamson, Blake A. Simmons & Pamela C. Ronald

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  1. Paul Tanger
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  2. Miguel E. Vega-Sánchez
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  9. Elizabeth B. Naredo
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  13. Kenneth L. McNally
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  14. Blake A. Simmons
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  15. Pamela C. Ronald
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Corresponding author

Correspondence to Jan E. Leach.

Electronic supplementary material

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ESM 1

(DOCX 26 kb)

Supplemental Table S1

Varieties used in this study along with the International Rice Genebank Collection (IRGC) number, country of origin, varietal group and class. (XLSX 11 kb)

Supplemental Table S2

Mean ± SD of traits measured on five varieties grown in GH and field. Pairwise F test p values of contrasts between environments: * p < 0.05, ** p < 0.01. F tests and SD are not shown for Azucena in the GH since n = 1. If SD < 0.05 it was rounded to 0 in this table. (XLSX 14 kb)

Supplemental Table S3

MLG, AcBr lignin, cell wall monosaccharide composition of stem tissue of 20 rice varieties grown in the GH. (XLSX 21 kb)

Supplemental Table S4

Sugar yield of 20 rice varieties grown in the GH. Combined tissue is the whole plant. (XLSX 13 kb)

Supplemental Table S5

Stem wall thickness of 16 rice varieties grown in the GH, measured from the base of the plant. Methods for this data is described in the Supplemental Methods. (XLSX 13 kb)

Supplemental Table S6

Elemental composition of leaf, sheath, and seed of 20 varieties of rice grown in the field and reported in parts per million (ppm). Methods for this data is described in the Supplemental Methods. ) (XLSX 18 kb)

Supplemental Table S7

Statistics of linear models to estimate glucose yield after dilute base pretreatment in the field, from parameters measured in the GH. A Shapiro-Wilk test of normality of the residuals was completed for each model and the p value is reported here. (XLSX 9 kb)

Supplemental Table S8

Matrix of Spearman’s correlations coefficients calculated from mean values of each variety for all traits measured (* p < 0.05, ** p < 0.01). (XLSX 10 kb)

Supplemental Table S9

Matrix of Spearman’s correlations coefficients calculated from mean values of each variety for all traits measured, separately for each environment (* p < 0.05, ** p < 0.01). (XLSX 14 kb)

Supplemental Table S10

Model selection parameters and statistics estimating glucose yield after base pretreatment from forage compositional traits. The intercept and coefficient for each term is listed for each model from Table 4, as well as the AICc and ∆AICc. Parameters not part of a model do not have a coefficient reported. (XLSX 55 kb)

Supplemental Table S11

Model selection parameters and statistics estimating glucose yield after base pretreatment from NREL hydrolysis compositional traits. The intercept and coefficient for each term is listed for each model from Table 5, as well as the AICc and ∆AICc. Parameters not part of a model do not have a coefficient reported. (XLSX 53 kb)

Supplemental Fig. S1

Pearson’s correlations and scatterplots of selected data from means of 20 rice varieties grown in the GH. The correlation coefficients and p values are reported at the top of the matrix. Glucose (Glu) and Pentose (Pen) yield are from whole plant samples, and saccharification (Sacc) yield, Sacc percent and MLG are from stem samples, from different plants. Sacc percent is sugars as a percent of as received (AR) dry weight. (PDF 177 kb)

Supplemental Fig. S2

Cellulose, lignin, hemicelluloses and ash means for leaf and stem tissue from five rice varieties grown in either greenhouse (GH) or field environments. Bars ± SD (PDF 173 kb)

Supplemental Fig. S3

Components of NREL hydrolysis of biomass as total glucose, total xylose, Klason lignin and Klason ash. Means for leaf and stem tissue from five rice varieties grown in either greenhouse (GH) or field environments. Bars ± SD (PDF 211 kb)

Supplemental Fig. S4

Glucose (a) and pentose (b) yield efficiency, as percent of total glucose and xylose content. Means for leaf and stem tissue from five rice varieties grown in either greenhouse (GH) or field environments. Bars ± SD (PDF 384 kb)

Supplemental Fig. S5

Soluble free glucose measured with no pretreatment. Means for leaf and stem tissue from five varieties grown in either greenhouse (GH) or field environments. Bars ± SD (PDF 138 kb)

Supplemental Fig. S6

Mixed Linkage Glucan (MLG). Means for leaf and stem tissue from five varieties in either greenhouse (GH) or field environment. Bars ± SD (PDF 133 kb)

Supplemental Fig. S7

Hydroxyproline rich glycoproteins (HRGP). Means for leaf and stem tissue from five varieties in either greenhouse (GH) or field environment. Bars ± SD (PDF 137 kb)

Supplemental Fig. S8

Bulk density of finely ground biomass. Means for leaf and stem tissue from five rice varieties grown in either greenhouse (GH) or field environments. Bars ± SD (PDF 134 kb)

Supplemental Fig. S9

Matrix of correlations calculated from mean values of each variety for traits measured from plants grown in the field and GH. Spearman’s rank correlation coefficients are indicated with the shape of the ellipse and the scale bar ranges. Insignificant correlations are indicated with the p value (p > 0.05) or with an asterick for insignificant Bonferroni adjusted p values (0.0018 < p < 0.05). (PDF 176 kb)

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Tanger, P., Vega-Sánchez, M.E., Fleming, M. et al. Cell Wall Composition and Bioenergy Potential of Rice Straw Tissues Are Influenced by Environment, Tissue Type, and Genotype. Bioenerg. Res. 8, 1165–1182 (2015). https://doi.org/10.1007/s12155-014-9573-y

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