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

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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References

  1. 1.

    National Research Council (2011) Renewable fuel standard: potential economic and environmental effects of u.s. biofuel policy. The National Academies Press, Washington

    Google Scholar 

  2. 2.

    Torres AF, van der Weijde T, Dolstra O et al (2013) Effect of maize biomass composition on the optimization of dilute-acid pretreatments and enzymatic saccharification. BioEnergy Res 6:1038–1051. doi:10.1007/s12155-013-9337-0

    CAS  Article  Google Scholar 

  3. 3.

    Lindedam J, Andersen SB, DeMartini J et al (2012) Cultivar variation and selection potential relevant to the production of cellulosic ethanol from wheat straw. Biomass Bioenergy 37:221–228. doi:10.1016/j.biombioe.2011.12.009

    CAS  Article  Google Scholar 

  4. 4.

    Carpita NC (2012) Progress in the biological synthesis of the plant cell wall: new ideas for improving biomass for bioenergy. Curr Opin Biotechnol 23:330–337. doi:10.1016/j.copbio.2011.12.003

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Nookaraju A, Pandey SK, Bae H-J, Joshi CP (2013) Designing cell walls for improved bioenergy production. Mol Plant 6:8–10. doi:10.1093/mp/sss111

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Yang F, Mitra P, Zhang L et al (2012) Engineering secondary cell wall deposition in plants. Plant Biotechnol J 11:325–335. doi:10.1111/pbi.12016

    PubMed Central  Article  PubMed  Google Scholar 

  7. 7.

    Lv S, Yu Q, Zhuang X et al (2013) The influence of hemicellulose and lignin removal on the enzymatic digestibility from sugarcane bagasse. BioEnergy Res 6:1128–1134. doi:10.1007/s12155-013-9297-4

    CAS  Article  Google Scholar 

  8. 8.

    Vogel J (2008) Unique aspects of the grass cell wall. Curr Opin Plant Biol 11:301–307. doi:10.1016/j.pbi.2008.03.002

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Sarkar P, Bosneaga E, Auer M (2009) Plant cell walls throughout evolution: towards a molecular understanding of their design principles. J Exp Bot 60:3615–3635. doi:10.1093/jxb/erp245

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Tao G, Lestander TA, Geladi P, Xiong S (2012) Biomass properties in association with plant species and assortments i: a synthesis based on literature data of energy properties. Renew Sustain Energy Rev 16:3481–3506. doi:10.1016/j.rser.2012.02.039

    CAS  Article  Google Scholar 

  11. 11.

    Jin S, Chen H (2006) Structural properties and enzymatic hydrolysis of rice straw. Process Biochem 41:1261–1264. doi:10.1016/j.procbio.2005.12.022

    CAS  Article  Google Scholar 

  12. 12.

    Bhandari HS, Walker DW, Bouton JH, Saha MC (2013) Effects of ecotypes and morphotypes in feedstock composition of switchgrass (panicum virgatum l.). GCB Bioenergy 6:26–34. doi:10.1111/gcbb.12053

    Article  Google Scholar 

  13. 13.

    Zhang H, Fangel JU, Willats WGT et al (2014) Assessment of leaf/stem ratio in wheat straw feedstock and impact on enzymatic conversion. GCB Bioenergy 6:90–96. doi:10.1111/gcbb.12060

    CAS  Article  Google Scholar 

  14. 14.

    Rancour DM, Marita JM, Hatfield RD (2012) Cell wall composition throughout development for the model grass brachypodium distachyon. Front Plant Biotechnol 3:266. doi:10.3389/fpls.2012.00266

    Google Scholar 

  15. 15.

    Fangel JU, Ulvskov P, Knox JP et al (2012) Cell wall evolution and diversity. Front Plant Sci 3:152. doi:10.3389/fpls.2012.00152

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  16. 16.

    Popper ZA, Michel G, Hervé C et al (2011) Evolution and diversity of plant cell walls: from algae to flowering plants. Annu Rev Plant Biol 62:567–590. doi:10.1146/annurev-arplant-042110-103809

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Zhang S-J, Song X-Q, Yu B-S et al (2012) Identification of quantitative trait loci affecting hemicellulose characteristics based on cell wall composition in a wild and cultivated rice species. Mol Plant 5:162–175. doi:10.1093/mp/ssr076

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Mann DGJ, Labbé N, Sykes RW et al (2009) Rapid assessment of lignin content and structure in switchgrass (panicum virgatum l.) grown under different environmental conditions. BioEnergy Res 2:246–256. doi:10.1007/s12155-009-9054-x

    Article  Google Scholar 

  19. 19.

    Serapiglia MJ, Cameron KD, Stipanovic AJ et al (2013) Yield and woody biomass traits of novel shrub willow hybrids at two contrasting sites. BioEnergy Res 6:533–546. doi:10.1007/s12155-012-9272-5

    Article  Google Scholar 

  20. 20.

    Hopkins AA, Vogel KP, Moore KJ et al (1995) Genotype effects and genotype by environment interactions for traits of elite switchgrass populations. Crop Sci 35:125–132. doi:10.2135/cropsci1995.0011183X003500010023x

    Article  Google Scholar 

  21. 21.

    Abou-El-Enin O, Fadel J, Mackill D (1999) Differences in chemical composition and fibre digestion of rice straw with, and without, anhydrous ammonia from 53 rice varieties. Anim Feed Sci Technol 79:129–136. doi:10.1016/S0377-8401(98)00271-5

    CAS  Article  Google Scholar 

  22. 22.

    Barriere Y, Guillet C, Goffner D, Pichon M (2003) Genetic variation and breeding strategies for improved cell wall digestibility in annual forage crops. a review. Anim Res 52:193–228. doi:10.1051/animres:2003018

    CAS  Article  Google Scholar 

  23. 23.

    Ioelovich M (2014) Correlation analysis of enzymatic digestibility of plant biomass. Biomass Convers Biorefinery 1–7. doi: 10.1007/s13399-013-0109-z

  24. 24.

    Studer MH, DeMartini JD, Davis MF et al (2011) Lignin content in natural populus variants affects sugar release. Proc Natl Acad Sci 108:6300–6305. doi:10.1073/pnas.1009252108

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  25. 25.

    Bottcher A, Cesarino I, Santos AB et al (2013) Lignification in sugarcane: biochemical characterization, gene discovery and expression analysis in two genotypes contrasting for lignin content. Plant Physiol 163:1539–1557. doi:10.1104/pp. 113.225250

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  26. 26.

    Xu N, Zhang W, Ren S et al (2012) Hemicelluloses negatively affect lignocellulose crystallinity for high biomass digestibility under naoh and h2so4 pretreatments in miscanthus. Biotechnol Biofuels 5:1–12. doi:10.1186/1754-6834-5-58

    Article  Google Scholar 

  27. 27.

    Vega-Sánchez ME, Verhertbruggen Y, Christensen U et al (2012) Loss of cellulose synthase-like f6 function affects mixed-linkage glucan deposition, cell wall mechanical properties, and defense responses in vegetative tissues of rice. Plant Physiol 159:56–69. doi:10.1104/pp. 112.195495

    PubMed Central  Article  PubMed  Google Scholar 

  28. 28.

    Bartley LE, Peck ML, Kim S-R et al (2013) Overexpression of a bahd acyltransferase, osat10, alters rice cell wall hydroxycinnamic acid content and saccharification. Plant Physiol 161:1615–1633. doi:10.1104/pp. 112.208694

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  29. 29.

    Buanafina MMDO, Langdon T, Hauck B et al (2008) Expression of a fungal ferulic acid esterase increases cell wall digestibility of tall fescue (festuca arundinacea). Plant Biotechnol J 6:264–280. doi:10.1111/j.1467-7652.2007.00317.x

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Xue J, Bosch M, Knox JP (2013) Heterogeneity and glycan masking of cell wall microstructures in the stems of miscanthus x giganteus, and its parents m. sinensis and m. sacchariflorus. PLoS ONE 8:e82114. doi:10.1371/journal.pone.0082114

    PubMed Central  Article  PubMed  Google Scholar 

  31. 31.

    Vega-Sanchez ME, Verhertbruggen Y, Scheller HV, Ronald PC (2013) Abundance of mixed linkage glucan in mature tissues and secondary cell walls of grasses. Plant Signal Behav 8:e23143. doi:10.4161/psb.23143

    PubMed Central  Article  PubMed  Google Scholar 

  32. 32.

    Lamport DTA, Kieliszewski MJ, Chen Y, Cannon MC (2011) Role of the extensin superfamily in primary cell wall architecture. Plant Physiol 156:11–19. doi:10.1104/pp. 110.169011

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  33. 33.

    Heath MF, Northcote DH (1971) Glycoprotein of the wall of sycamore tissue-culture cells. Biochem J 125:953–961

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  34. 34.

    Lamport DTA (1980) Structure and function of plant glycoproteins. In: Stumpf P (ed) Biochem. plants a Compr. treatise, 3rd edn. Academic, New York, pp 501–541

    Google Scholar 

  35. 35.

    Santoro N, Cantu SL, Tornqvist C-E et al (2010) A high-throughput platform for screening milligram quantities of plant biomass for lignocellulose digestibility. BioEnergy Res 3:93–102. doi:10.1007/s12155-009-9074-6

    Article  Google Scholar 

  36. 36.

    Chevanan N, Womac AR, Bitra VSP et al (2010) Bulk density and compaction behavior of knife mill chopped switchgrass, wheat straw, and corn stover. Bioresour Technol 101:207–214. doi:10.1016/j.biortech.2009.07.083

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Wu Z, Zhang M, Wang L et al (2013) Biomass digestibility is predominantly affected by three factors of wall polymer features distinctive in wheat accessions and rice mutants. Biotechnol Biofuels 6:183. doi:10.1186/1754-6834-6-183

    PubMed Central  Article  PubMed  Google Scholar 

  38. 38.

    Timpano H, Sibout R, Devaux M-F et al (2014) Brachypodium cell wall mutant with enhanced saccharification potential despite increased lignin content. BioEnergy Res. doi:10.1007/s12155-014-9501-1

    Google Scholar 

  39. 39.

    Cosgrove DJ, Jarvis MC (2012) Comparative structure and biomechanics of plant primary and secondary cell walls. Front Plant Sci 3:204. doi:10.3389/fpls.2012.00204

    PubMed Central  Article  PubMed  Google Scholar 

  40. 40.

    McNally KL, Bruskiewich R, Mackill D et al (2006) Sequencing multiple and diverse rice varieties. connecting whole-genome variation with phenotypes. Plant Physiol 141:26–31. doi:10.1104/pp.106.077313

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  41. 41.

    McNally KL, Childs KL, Bohnert R et al (2009) Genomewide snp variation reveals relationships among landraces and modern varieties of rice. Proc Natl Acad Sci. doi:10.1073/pnas.0900992106

    Google Scholar 

  42. 42.

    Jahn CE, Mckay JK, Mauleon R et al (2011) Genetic variation in biomass traits among 20 diverse rice varieties. Plant Physiol 155:157–168. doi:10.1104/pp. 110.165654

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  43. 43.

    Dobermann A, Dawe D, Roetter RP, Cassman KG (2000) Reversal of rice yield decline in a long-term continuous cropping experiment. Agron J 92:633–643

    Article  Google Scholar 

  44. 44.

    Van Soest PJ, Robertson JB, Lewis BA (1991) Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J Dairy Sci 74:3583–3597. doi:10.3168/jds.S0022-0302(91)78551-2

    Article  PubMed  Google Scholar 

  45. 45.

    Sluiter A, Hames B, Ruiz R et al (2008) Determination of structural carbohydrates and lignin in biomass. National Renewable Energy Laboratory, Golden

    Google Scholar 

  46. 46.

    Miller GL (1959) Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal Chem 31:426–428. doi:10.1021/ac60147a030

    CAS  Article  Google Scholar 

  47. 47.

    Verhertbruggen Y, Marcus SE, Haeger A et al (2009) An extended set of monoclonal antibodies to pectic homogalacturonan. Carbohydr Res 344:1858–1862. doi:10.1016/j.carres.2008.11.010

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    Kivirikko KI, Liesmaa M (1959) A colorimetric method for determination of hydroxyproline in tissue hydrolysates. Scand J Clin Lab Invest 11:128–133

    CAS  Article  Google Scholar 

  49. 49.

    R core Team (2013) R: a language and environment for statistical computing. r foundation for statistical computing, vienna, austria. http://www.r-project.org/.

  50. 50.

    Ghasemi E, Ghorbani GR, Khorvash M et al (2013) Chemical composition, cell wall features and degradability of stem, leaf blade and sheath in untreated and alkali-treated rice straw. Animal 7:1106–1112. doi:10.1017/S1751731113000256

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    Liu L, Ye XP, Womac AR, Sokhansanj S (2010) Variability of biomass chemical composition and rapid analysis using ft-nir techniques. Carbohydr Polym 81:820–829. doi:10.1016/j.carbpol.2010.03.058

    CAS  Article  Google Scholar 

  52. 52.

    Rozema J, van de Staaij J, Björn LO, Caldwell M (1997) Uv-b as an environmental factor in plant life: stress and regulation. Trends Ecol Evol 12:22–28. doi:10.1016/S0169-5347(96)10062-8

    CAS  Article  PubMed  Google Scholar 

  53. 53.

    Hahlbrock K, Grisebach H (1979) Enzymic controls in the biosynthesis of lignin and flavanoids. Annu Rev Plant Physiol 30:105–130

    CAS  Article  Google Scholar 

  54. 54.

    Moura JCMS, Bonine CAV, de Oliveira Fernandes Viana J et al (2010) Abiotic and biotic stresses and changes in the lignin content and composition in plants. J Integr Plant Biol 52:360–376. doi:10.1111/j.1744-7909.2010.00892.x

    CAS  Article  PubMed  Google Scholar 

  55. 55.

    Cipollini DF Jr (1997) Wind-induced mechanical stimulation increases pest resistance in common bean. Oecologia 111:84–90. doi:10.1007/s004420050211

    Article  Google Scholar 

  56. 56.

    Dien BS, Jung H-JG, Vogel KP et al (2006) Chemical composition and response to dilute-acid pretreatment and enzymatic saccharification of alfalfa, reed canarygrass, and switchgrass. Biomass Bioenergy 30:880–891. doi:10.1016/j.biombioe.2006.02.004

    CAS  Article  Google Scholar 

  57. 57.

    Jung HG, Mertens DR, Payne AJ (1997) Correlation of acid detergent lignin and klason lignin with digestibility of forage dry matter and neutral detergent fiber. J Dairy Sci 80:1622–1628. doi:10.3168/jds.S0022-0302(97)76093-4

    CAS  Article  PubMed  Google Scholar 

  58. 58.

    Vogel KP, Mitchell RB, Sarath G et al (2013) Switchgrass biomass composition altered by six generations of divergent breeding for digestibility. Crop Sci 53:853. doi:10.2135/cropsci2012.09.0542

    Article  Google Scholar 

  59. 59.

    Brodeur G, Yau E, Badal K et al (2011) Chemical and physicochemical pretreatment of lignocellulosic biomass: a review. Enzym Res 2011:787532. doi:10.4061/2011/787532

    Google Scholar 

  60. 60.

    Chen W-H, Chen Y-C, Lin J-G (2014) Study of chemical pretreatment and enzymatic saccharification for producing fermentable sugars from rice straw. Bioprocess Biosyst Eng 37:1337–1344. doi:10.1007/s00449-013-1106-0

    CAS  Article  PubMed  Google Scholar 

  61. 61.

    Haydon MJ, Bell LJ, Webb AAR (2011) Interactions between plant circadian clocks and solute transport. J Exp Bot 62:2333–2348. doi:10.1093/jxb/err040

    CAS  Article  PubMed  Google Scholar 

  62. 62.

    Park J, Kanda E, Fukushima A et al (2011) Contents of various sources of glucose and fructose in rice straw, a potential feedstock for ethanol production in japan. Biomass Bioenergy 35:3733–3735. doi:10.1016/j.biombioe.2011.05.032

    CAS  Article  Google Scholar 

  63. 63.

    Serapiglia MJ, Humiston MC, Xu H et al (2013) Enzymatic saccharification of shrub willow genotypes with differing biomass composition for biofuel production. Front Plant Sci 4:57. doi:10.3389/fpls.2013.00057

    PubMed Central  Article  PubMed  Google Scholar 

  64. 64.

    Dien BS, Sarath G, Pedersen JF et al (2009) Improved sugar conversion and ethanol yield for forage sorghum (sorghum bicolor l. moench) lines with reduced lignin contents. BioEnergy Res 2:153–164. doi:10.1007/s12155-009-9041-2

    Article  Google Scholar 

  65. 65.

    Chen F, Dixon RA (2007) Lignin modification improves fermentable sugar yields for biofuel production. Nat Biotechnol 25:759–761. doi:10.1038/nbt1316

    CAS  Article  PubMed  Google Scholar 

  66. 66.

    Shen H, Fu C, Xiao X et al (2009) Developmental control of lignification in stems of lowland switchgrass variety alamo and the effects on saccharification efficiency. BioEnergy Res 2:233–245. doi:10.1007/s12155-009-9058-6

    Article  Google Scholar 

  67. 67.

    Sarath G, Dien B, Saathoff AJ et al (2011) Ethanol yields and cell wall properties in divergently bred switchgrass genotypes. Bioresour Technol 102:9579–9585. doi:10.1016/j.biortech.2011.07.086

    CAS  Article  PubMed  Google Scholar 

  68. 68.

    Rollin JA, Zhu Z, Sathitsuksanoh N, Zhang Y-HP (2011) Increasing cellulose accessibility is more important than removing lignin: a comparison of cellulose solvent-based lignocellulose fractionation and soaking in aqueous ammonia. Biotechnol Bioeng 108:22–30. doi:10.1002/bit.22919

    CAS  Article  PubMed  Google Scholar 

  69. 69.

    Ray M, Brereton N, Shield I et al (2012) Variation in cell wall composition and accessibility in relation to biofuel potential of short rotation coppice willows. BioEnergy Res 5:685–698. doi:10.1007/s12155-011-9177-8

    Article  Google Scholar 

  70. 70.

    Tanger P, Field JL, Jahn CE et al (2013) Biomass for thermochemical conversion: targets and challenges. Front Plant Sci 4:218. doi:10.3389/fpls.2013.00218

    PubMed Central  Article  PubMed  Google Scholar 

  71. 71.

    Goto M, Ehara H, Karita S et al (2003) Protective effect of silicon on phenolic biosynthesis and ultraviolet spectral stress in rice crop. Plant Sci 164:349–356. doi:10.1016/S0168-9452(02)00419-3

    CAS  Article  Google Scholar 

  72. 72.

    Schaller J, Brackhage C, Dudel EG (2012) Silicon availability changes structural carbon ratio and phenol content of grasses. Environ Exp Bot 77:283–287. doi:10.1016/j.envexpbot.2011.12.009

    CAS  Article  Google Scholar 

  73. 73.

    Suzuki S, Ma JF, Yamamoto N et al (2012) Silicon deficiency promotes lignin accumulation in rice. Plant Biotechnol 29:391–394. doi:10.5511/plantbiotechnology.12.0416a

    CAS  Article  Google Scholar 

  74. 74.

    Schoelynck J, Bal K, Backx H et al (2010) Silica uptake in aquatic and wetland macrophytes: a strategic choice between silica, lignin and cellulose? New Phytol 186:385–391. doi:10.1111/j.1469-8137.2009.03176.x

    CAS  Article  PubMed  Google Scholar 

  75. 75.

    Schaller J, Brackhage C, Bäucker E, Dudel EG (2013) Uv-screening of grasses by plant silica layer? J Biosci 38:413–416. doi:10.1007/s12038-013-9303-1

    Article  PubMed  Google Scholar 

  76. 76.

    Datnoff LE, Snyder GH, Korndörfer GH (2001) Silicon in agriculture, 1st edn. Elsevier Science, New York

    Google Scholar 

  77. 77.

    Baxter HL, Mazarei M, Labbe N et al (2014) Two-year field analysis of reduced recalcitrance transgenic switchgrass. Plant Biotechnol J. doi:10.1111/pbi.12195

    Google Scholar 

  78. 78.

    Couhert C, Commandre J-M, Salvador S (2009) Is it possible to predict gas yields of any biomass after rapid pyrolysis at high temperature from its composition in cellulose, hemicellulose and lignin? Fuel 88:408–417. doi:10.1016/j.fuel.2008.09.019

    CAS  Article  Google Scholar 

  79. 79.

    Rath J, Heuwinkel H, Herrmann A (2013) Specific biogas yield of maize can be predicted by the interaction of four biochemical constituents. BioEnergy Res 6:939–952. doi:10.1007/s12155-013-9318-3

    CAS  Article  Google Scholar 

  80. 80.

    Oh C-S, Kim H, Lee C (2013) Rice cell wall polysaccharides: structure and biosynthesis. J Plant Biol 56:274–282. doi:10.1007/s12374-013-0236-x

    CAS  Article  Google Scholar 

  81. 81.

    Qi X, Behrens BX, West PR, Mort AJ (1995) Solubilization and partial characterization of extensin fragments from cell walls of cotton suspension cultures. evidence for a covalent cross- link between extensin and pectin. Plant Physiol 108:1691–1701. doi:10.1104/pp. 108.4.1691

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  82. 82.

    Liang H, Frost CJ, Wei X et al (2008) Improved sugar release from lignocellulosic material by introducing a tyrosine-rich cell wall peptide gene in poplar. Clean Soil Air Water 36:662–668. doi:10.1002/clen.200800079

    CAS  Article  Google Scholar 

  83. 83.

    Brady JD, Sadler IH, Fry SC (1996) Di-isodityrosine, a novel tetrametric derivative of tyrosine in plant cell wall proteins: a new potential cross-link. Biochem J 315:323–327

    PubMed Central  CAS  Article  PubMed  Google Scholar 

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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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Corresponding author

Correspondence to Jan E. Leach.

Electronic supplementary material

Below is the link to the electronic supplementary material.

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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Keywords

  • Environmental variation
  • Mixed linkage glucan
  • Saccharification efficiency
  • HRGPs
  • Density
  • Forage