Skip to main content

Advertisement

SpringerLink
Log in

Estimation of Genetic Parameters of Biomass Production and Composition Traits in Miscanthus sinensis Using a Staggered-Start Design

  • Published:
BioEnergy Research Aims and scope Submit manuscript

Abstract

Traits for biomass production and composition make Miscanthus a promising bioenergy crop for different bioconversion routes. They need to be considered in miscanthus breeding programs as they are subjected to genetic and genetic × environment factors. The objective was to estimate the genetic parameters of an M. sinensis population grown during 4 years in two French locations. In each location, the experiment was established according to a staggered-start design in order to decompose the year effect into age and climate effects. Linear mixed models were used to estimate genetic variance, genotype × age, genotype × climate interaction variances, and residual variances. Individual plant broad-sense heritability means ranged from 0.42 to 0.62 for biomass production traits and were more heritable than biomass composition traits with means ranging from 0.26 to 0.47. Heritability increased through age for most of the biomass production and composition traits. Low genetic variance along with large genotype × age and genotype × climate interaction variances tended to decrease the heritability of biomass production traits for young plant ages. Most of the production traits showed large interaction variances for age and climate in both locations, while biomass composition traits highlighted large interaction variances due to climate in Orléans. The genetic and phenotypic correlations between biomass production and composition traits were positive, while hemicelluloses were negatively correlated with all traits. Selection is difficult on young plants as the heritability is too low. The joint improvement of biomass production and composition traits would help provide a better response of miscanthus to selection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

This work was performed with the facilities of the Plant Bioinformatics Facility of INRAE-URGI (https://doi.org/10.15454/1.5572414581735654E12)and

(https://doi.org/10.15454/1NVRNJ) for datasets related to phenotyping resources.

These data are common to those of paper [72].

Code Availability

Not applicable.

Abbreviations

ABM_tDMha:

Aboveground biomass yield

ADF_%DM:

Acid detergent fiber

ADL_%DM – ADL_%CW:

Acid detergent lignin

Ash_%DM:

Ash

CH_cm:

Canopy height

CGDD:

Cumulated growing degree-days

CL_%DM – CL_%CW:

Cellulose

CW:

Cell wall

C50_cm:

Plant circumference

DM:

Dry matter

ETPP:

Penman potential evapotranspiration

GBFOr:

Unité expérimentale Génétique Biomasse Forestières Orléans

GCIE:

Unité expérimentale Grandes Cultures Innovation Environnement

GnpIS:

Multispecies integrative information system

GWAS:

Genome-wide association study

\({\mathrm{H}}_{sl}^{2}\) :

Individual plant broad-sense heritability

\({\mathrm{H}}_{Pi}^{2}\) :

Progeny-mean broad-sense heritability

HEM_%DM – HEM_%CW:

Hemicelluloses

HMax_cm:

Plant maximum height

IJPB:

Institut Jean-Pierre Bourgin

MaxT:

Air maximum temperature

Mal:

Malepartus

MeanH:

Mean humidity

MeanT:

Air mean temperature

MinT:

Air minimum temperature

MinTs:

Soil minimum temperature

NDF_%DM:

Neutral detergent fiber

PAR:

Photosynthetically active radiation

PCA:

Principal component analysis

Prec_M:

Mean precipitation

Prec_S:

Cumulated precipitation

PSNb:

Plant stem number

QTL:

Quantitative trait loci

Sil:

Silberspinne

URGI:

Unité de Recherche Génomique-Info

VPD:

Vapor-pressure deficit

VPD_M:

Maximum vapor-pressure deficit

References

  1. Foley JA, Ramankutty N, Brauman KA et al (2011) Solutions for a cultivated planet. Nature 478:337–342. https://doi.org/10.1038/nature10452

    Article  CAS  PubMed  Google Scholar 

  2. Gabrielle B, Bamière L, Caldes N et al (2014) Paving the way for sustainable bioenergy in Europe: technological options and research avenues for large-scale biomass feedstock supply. Renew Sustain Energy Rev 33:11–25. https://doi.org/10.1016/j.rser.2014.01.050

    Article  Google Scholar 

  3. Lewandowski I, Clifton-Brown JC, Scurlock JMO, Huisman W (2000) Miscanthus: European experience with a novel energy crop. Biomass Bioenerg 19:209–227. https://doi.org/10.1016/S0961-9534(00)00032-5

    Article  CAS  Google Scholar 

  4. Lewandowski I, Scurlock JMO, Lindvall E, Christou M (2003) The development and current status of perennial rhizomatous grasses as energy crops in the US and Europe. Biomass Bioenerg 25:335–361. https://doi.org/10.1016/S0961-9534(03)00030-8

    Article  Google Scholar 

  5. Clifton-Brown JC, Stampfl PF, Jones MB (2004) Miscanthus biomass production for energy in Europe and its potential contribution to decreasing fossil fuel carbon emissions. Glob Chang Biol 10:509–518. https://doi.org/10.1111/j.1529-8817.2003.00749.x

    Article  Google Scholar 

  6. Hastings A, Clifton-Brown JC, Wattenbach M et al (2009) Future energy potential of Miscanthus in Europe. GCB Bioenergy 1:180–196. https://doi.org/10.1111/j.1757-1707.2009.01012.x

    Article  Google Scholar 

  7. Jones M, Walsh M (2001) Miscanthus for energy and fibre. James & James, Science Publishers, London.

  8. Heaton EA, Clifton-Brown JC, Voigt TB et al (2004) Miscanthus for renewable energy generation: European Union experience and projections for Illinois. Mitig Adapt Strateg Glob Chang 9:433–451. https://doi.org/10.1023/B:MITI.0000038848.94134.be

    Article  Google Scholar 

  9. Girones J, Vo L, Arnoult S et al (2016) Miscanthus stem fragment – reinforced polypropylene composites: development of an optimized preparation procedure at small scale and its validation for differentiating genotypes. Polym Test 55:166–172. https://doi.org/10.1016/j.polymertesting.2016.08.023

    Article  CAS  Google Scholar 

  10. Greef J, Deuter M (1993) Syntaxonomy of Miscanthus x giganteus Greed et Deu. Angew Bot 67:87–90

    Google Scholar 

  11. Acikel H (2011) The use of miscanthus (Giganteus) as a plant fiber in concrete production. Sci Res Essays 6:2660–2667. https://doi.org/10.5897/SRE10.1139

    Article  Google Scholar 

  12. Anderson E, Arundale R, Maughan M et al (2011) Growth and agronomy of Miscanthus × giganteus for biomass production. Biofuels 2:167–183. https://doi.org/10.4155/bfs.10.80

    Article  CAS  Google Scholar 

  13. Hodkinson TR, Klaas M, Jones MB et al (2015) Miscanthus: a case study for the utilization of natural genetic variation. Plant Genet Resour Characterisation Util 13:219–237. https://doi.org/10.1017/S147926211400094X

    Article  Google Scholar 

  14. Arnoult S, Brancourt-Hulmel M (2015) A review on miscanthus biomass production and composition for bioenergy use: genotypic and environmental variability and implications for breeding. Bioenergy Res 8:502–526. https://doi.org/10.1007/s12155-014-9524-7

    Article  CAS  Google Scholar 

  15. Clifton-Brown JC, Harfouche A, Casler MD et al (2019) Breeding progress and preparedness for mass-scale deployment of perennial lignocellulosic biomass crops switchgrass, miscanthus, willow and poplar. GCB Bioenergy 11:118–151. https://doi.org/10.1111/gcbb.12566

  16. Sacks EJ, Juvik JA, Lin Q et al (2013) The gene pool of miscanthus species and its improvement. In: Paterson A. (eds) Genomics of the Saccharinae. Plant Genetics and Genomics: Crops and Models, vol 11. Springer, New York, NY, pp 73–101. https://doi.org/10.1007/978-1-4419-5947-8_4

  17. Hodkinson TR, Petrunenko E, Klass M, Münnich C (2015b) New breeding collections of Miscanthus sinensis, M. sacchariflorus and hybrids from Primorsky Krai, Far Eastern Russia. In: Proceedings of the Biomass 2015: Perennial biomass crops for a resource constrained world, Springer, Cham., pp 105–118. https://doi.org/10.1007/978-3-319-44530-4_10

  18. Chae WB, Hong SJ, Gifford JM et al (2014) Plant morphology, genome size, and SSR markers differentiate five distinct taxonomic groups among accessions in the genus Miscanthus. GCB Bioenergy 6:646–660. https://doi.org/10.1111/gcbb.12101

    Article  CAS  Google Scholar 

  19. Clifton-Brown JC, Chiang Y, Hodkinson TR (2008) Miscanthus genetic resources and breeding potential. In: Vermerris W (ed) Genetic Improvement of Bioenergy Crops. Springer Science, pp 273–290. https://doi.org/10.1007/978-0-387-70805-8_10

  20. Sun Q, Lin Q, Yi Z-L et al (2010) A taxonomic revision of Miscanthus s.l. (Poaceae) from China. Bot J Linn Soc 164:178–220. https://doi.org/10.1111/j.1095-8339.2010.01082.x

    Article  Google Scholar 

  21. Clark LV, Brummer JE, Głowacka K et al (2014) A footprint of past climate change on the diversity and population structure of Miscanthus sinensis. Ann Bot 114:97–107. https://doi.org/10.1093/aob/mcu084

    Article  PubMed  PubMed Central  Google Scholar 

  22. Clark LV, Ryan Stewart J, Nishiwaki A et al (2015) Genetic structure of Miscanthus sinensis and Miscanthus sacchariflorus in Japan indicates a gradient of bidirectional but asymmetric introgression. J Exp Bot 66:4213–4225. https://doi.org/10.1093/jxb/eru511

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Clifton-Brown JC, Lewandowski I, Andersson B et al (2001) Performance of 15 Miscanthus genotypes at five sites in Europe. Agron J 93:1013–1019

    Article  Google Scholar 

  24. Zub HW, Arnoult S, Brancourt-Hulmel M (2010) Key traits for biomass production identified in different Miscanthus species at two harvest dates. Biomass Bioenerg 35:637–651. https://doi.org/10.1016/j.biombioe.2010.10.020

    Article  Google Scholar 

  25. Zub HW, Monod H, Béthencourt L, Brancourt-Hulmel M (2012) An index of competition reduces statistical variability and improves comparisons between genotypes of miscanthus. Bioenergy Res 5:829–840. https://doi.org/10.1007/s12155-012-9193-3

    Article  Google Scholar 

  26. Brancourt‐Hulmel M, Raverdy R, Girones J, et al (2021a) Variability of stem solidness among miscanthus genotypes and its role on mechanical properties of polypropylene composites. GCB Bioenergy 13:1576-1585. https://doi.org/10.1111/gcbb.12818

  27. Hatfield RD, Rancour DM, Marita JM (2017) Grass cell walls: a story of cross-linking. Front Plant Sci 7:1–15. https://doi.org/10.3389/fpls.2016.02056

  28. Clifton-Brown JC, Lewandowski I (2002) Screening miscanthus genotypes in field trials to optimise biomass yield and quality in Southern Germany. Eur J Agron 16:97–110. https://doi.org/10.1016/S1161-0301(01)00120-4

    Article  Google Scholar 

  29. Jezowski S, Głowacka K, Kaczmarek Z (2010) Variation on biomass yield and morphological traits of energy grasses from the genus Miscanthus during the first years of crop establishment. Biomass Bioenerg 35:814–821. https://doi.org/10.1016/j.biombioe.2010.11.013

    Article  Google Scholar 

  30. Anzoua KG, Suzuki K, Fujita S et al (2015) Evaluation of morphological traits, winter survival and biomass potential in wild Japanese Miscanthus sinensis Anderss. populations in northern Japan. Grassl Sci 61:83–91. https://doi.org/10.1111/grs.12085

    Article  Google Scholar 

  31. Arnoult S, Obeuf A, Béthencourt L, Mansard M (2015) Miscanthus clones for cellulosic bioethanol production: relationships between biomass production, biomass production components, and biomass chemical composition. Ind Crop Prod 63:316–328. https://doi.org/10.1016/j.indcrop.2014.10.011

    Article  CAS  Google Scholar 

  32. Kaiser CM, Clark LV, Juvik JA et al (2015) Characterizing a miscanthus germplasm collection for yield, yield components, and genotype × environment interactions. Crop Sci 55:1978–1994. https://doi.org/10.2135/2014.11.0805

    Article  CAS  Google Scholar 

  33. Hodgson EM, Lister SJ, Bridgwater AV et al (2010) Genotypic and environmentally derived variation in the cell wall composition of Miscanthus in relation to its use as a biomass feedstock. Biomass Bioenerg 34:652–660. https://doi.org/10.1016/j.biombioe.2010.01.008

    Article  CAS  Google Scholar 

  34. Allison GG, Morris C, Clifton-Brown JC et al (2011) Genotypic variation in cell wall composition in a diverse set of 244 accessions of Miscanthus. Biomass Bioenerg 35:4740–4747. https://doi.org/10.1016/j.biombioe.2011.10.008

    Article  CAS  Google Scholar 

  35. Iqbal Y, Lewandowski I (2014) Inter-annual variation in biomass combustion quality traits over five years in fifteen Miscanthus genotypes in south Germany. Fuel Process Technol 121:47–55. https://doi.org/10.1016/j.fuproc.2014.01.003

    Article  CAS  Google Scholar 

  36. Van der Weijde T, Dolstra O, Visser RGF, Trindade LM (2017) Stability of cell wall composition and saccharification efficiency in Miscanthus across diverse environments. Front Plant Sci 7:1–14. https://doi.org/10.3389/fpls.2016.02004

    Article  Google Scholar 

  37. Falconer DS (1981) Introduction to quantitative genetics. Ronald Press, New York

    Google Scholar 

  38. Jezowski S (2008) Yield traits of six clones of Miscanthus in the first 3 years following planting in Poland. Ind Crops Prod 27:65–68. https://doi.org/10.1016/j.indcrop.2007.07.013

    Article  Google Scholar 

  39. Robson P, Jensen E, Hawkins S et al (2013) Accelerating the domestication of a bioenergy crop : identifying and modelling morphological targets for sustainable yield increase in Miscanthus. J Exp Bot 64:4143–4155. https://doi.org/10.1093/jxb/ert225

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gallais A, Poly J (1990) Théorie de la sélection en amélioration des plantes. Masson, Collection Sciences Agronomiques

    Google Scholar 

  41. Slavov G, Robson P, Jensen E et al (2013a) Contrasting geographic patterns of genetic variation for molecular markers vs . phenotypic traits in the energy grass Miscanthus sinensis. GCB Bioenergy 5:562–571. https://doi.org/10.1111/gcbb.12025

  42. Slavov GT, Nipper R, Robson P et al (2013b) Genome-wide association studies and prediction of 17 traits related to phenology, biomass and cell wall composition in the energy grass Miscanthus sinensis. New Phytol 201:1227-1239. https://doi.org/10.1111/nph.12621

  43. Nie G, Huang L, Zhang X et al (2016) Marker-trait association for biomass yield of potential bio-fuel feedstock Miscanthus sinensis from Southwest. Front Plant Sci 7:1–13. https://doi.org/10.3389/fpls.2016.00802

  44. Clark LV, Dzyubenko E, Dzyubenko N et al (2016) Ecological characteristics and in situ genetic associations for yield-component traits of wild Miscanthus from eastern Russia. Ann Bot 118:941–955. https://doi.org/10.1093/aob/mcw137

  45. Clark LV, Dwiyanti MS, Anzoua KG et al (2019) Biomass yield in a genetically diverse Miscanthus sinensis germplasm panel evaluated at five locations revealed individuals with exceptional potential. GCB Bioenergy 11:1125–1145. https://doi.org/10.1111/gcbb.12606

    Article  Google Scholar 

  46. Gifford JM, Chae WB, Swaminathan K et al (2015) Mapping the genome of Miscanthus sinensis for QTL associated with biomass productivity. GCB Bioenergy 7:797–810. https://doi.org/10.1111/gcbb.12201

    Article  CAS  Google Scholar 

  47. Dong H, Liu S, Clark LV et al (2018) Genetic mapping of biomass yield in three interconnected Miscanthus populations. GCB Bioenergy 10:165–185. https://doi.org/10.1111/gcbb.12472

    Article  CAS  Google Scholar 

  48. Van der Weijde T, Kamei CLA, Severing EI et al (2017) Genetic complexity of miscanthus cell wall composition and biomass quality for biofuels. BMC Genomics 18:1–15. https://doi.org/10.1186/s12864-017-3802-7

    Article  CAS  Google Scholar 

  49. Loughin TM (2006) Improved experimental design and analysis for long-term experiments. Crop Sci 46:2492–2502. https://doi.org/10.2135/cropsci2006.04.0271

    Article  Google Scholar 

  50. Segura V, Cilas C, Costes E (2008) Dissecting apple tree architecture into genetic, ontogenetic and environmental effects : mixed linear modelling of repeated spatial and temporal measures. New Phytol 178:302–314. https://doi.org/10.1111/j.1469-8137.2007.02374.x

  51. Tejera M, Boersma N, Vanloocke A et al (2019) Multi-year and multi-site establishment of the perennial biomass crop Miscanthus × giganteus using a staggered start design to elucidate N response. Bioenergy Res 12:471–483. https://doi.org/10.1007/s12155-019-09985-6

    Article  CAS  Google Scholar 

  52. Rambaud C, Arnoult S, Bluteau A et al (2013) Shoot organogenesis in three Miscanthus species and evaluation for genetic uniformity using AFLP analysis. Plant Cell Tissue Organ Cult 113:437–448. https://doi.org/10.1007/s11240-012-0284-9

    Article  CAS  Google Scholar 

  53. Leroy J, Ferchaud F, Giauffret C, et al (2022) Miscanthus sinensis is as efficient as Miscanthus × Giganteus for nitrogen recycling in spite of smaller nitrogen fluxes. BioEnergy Res https://doi.org/10.1007/s12155-022-10408-2

  54. Hou W, Raverdy R, Mignot E, et al (2021) Estimating the genetic parameters of flowering time-related traits in a Miscanthus sinensis population tested with a staggered-start design. Bioenergy Res https://doi.org/10.1007/s12155-021-10328-7

  55. Tejera MD, Heaton EA (2017) Description and codification of Miscanthus × giganteus growth stages for phenological assessment. Front Plant Sci 8:1–12. https://doi.org/10.3389/fpls.2017.01726

    Article  Google Scholar 

  56. Kirby EJ, Appleyard M (1984) Cereal development guide. 2nd Edition. Arable Unit, National Agricultural Centre, Stoneleigh, Warwickshire

  57. Hesketh JD, Warrington IJ (1989) Corn growth response to temperature: rate and duration of lead emergence. Agron J 81:696–701

    Article  Google Scholar 

  58. Steinbach D, Alaux M, Amselem J et al (2013) GnpIS: an information system to integrate genetic and genomic data from plants and fungi. Database 2013:1–9. https://doi.org/10.1093/database/bat058

    Article  CAS  Google Scholar 

  59. Belmokhtar N, Arnoult S, Chabbert B et al (2017) Saccharification performances of miscanthus at the pilot and miniaturized assay scales: genotype and year variabilities according to the biomass composition. Front Plant Sci 8:1–13. https://doi.org/10.3389/fpls.2017.00740

    Article  Google Scholar 

  60. Van Soest PJ, Wine RH (1967) Use of detergents in the analysis of fibrous feeds. IV. Determination of plant cell-wall constituents. J AOAC Int 50:50–55. https://doi.org/10.1093/jaoac/50.1.50

    Article  Google Scholar 

  61. Henderson CR (1984) Applications of linear models in animal breeding. Univ Guelph, Guelph

    Google Scholar 

  62. Muñoz F, Sanchez L (2019) breedR: statistical methods for forest genetic resources analysts. R package version 0.12–4. https://famuvie.github.io/breedR/

  63. Costa e Silva J, Dutkowski GW, Gilmour AR, (2001) Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual. Can J For Res 31:1887–1893. https://doi.org/10.1139/cjfr-31-11-1887

    Article  Google Scholar 

  64. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Automat Contr 19:716–723. https://doi.org/10.1109/TAC.1974.1100705

    Article  Google Scholar 

  65. Gouvêa LRL, Silva GAP, Verardi CK et al (2013) Simultaneous selection of rubber yield and girth growth in young rubber trees. Ind Crops Prod 50:39–43. https://doi.org/10.1016/j.indcrop.2013.06.040

    Article  Google Scholar 

  66. Howe T, Saruul P, Davis J, Chen H (2000) Quantitative genetics of bud phenology, frost damage, and winter survival in an F 2 family of hybrid poplars. Theoretical and Applied Genetics 101:632–642. https://doi.org/10.1007/s001220051525

  67. Wei T, Simko V (2017) R package “corrplot”: visualization of a correlation matrix (version 0.84). https://github.com/taiyun/corrplot

  68. Le S, Josse J, Husson F (2008) FactoMineR: an R package for multivariate analysis. Journal of Statistical Software 25:1–18. https://doi.org/10.18637/jss.v025.i01

  69. Kar S, Weng TY, Nakashima T et al (2019) Field performance of Saccharum × Miscanthus intergeneric hybrids (Miscanes) under cool climatic conditions of Northern Japan. Bioenergy Res 13:132-146. https://doi.org/10.1007/s12155-019-10066-x

  70. Petit J, Salentijn EMJ, Paulo MJ et al (2020) Genetic variability of morphological, flowering, and biomass quality traits in hemp (Cannabis sativa L.). Front Plant Sci 11:1–17. https://doi.org/10.3389/fpls.2020.00102

    Article  Google Scholar 

  71. Brancourt-Hulmel M, Arnoult S, Cézard L, et al (2021b) A comparative study of maize and miscanthus regarding cell-wall composition and stem anatomy for conversion into bioethanol and polymer composites. BioEnergy Res https://doi.org/10.1007/s12155-020-10239-z

  72. Raverdy R, Lourgant K, Mignot E, et al (2022) Linkage mapping of biomass production and composition traits in a Miscanthus sinensis population. BioEnergy Res https://doi.org/10.1007/s12155-022-10402-8

Download references

Acknowledgements

The authors wish to acknowledge each member of the staff who worked for the establishment and phenotyping of each trial in INRAE experimental stations: GCIE–Picardie team in Estrées-Mons, GBFOr team in Orléans, and IJPB team in Versailles. The authors thank Rebecca James and Andrea Rau who edited the English text.

Funding

This work has benefited from the support of the Investments for the Future program (grant ANR-11-BTBR-0006-BFF) managed by the French National Research Agency (Agence Nationale de la Recherche, ANR).

Author information

Authors and Affiliations

  1. BioEcoAgro Joint Research Unit, INRAE AgroImpact, University of Liège, University of Lille, University of Picardie Jules Verne, 80203, Estrées-Mons, France

    Raphaël Raverdy, Laura Fingar, Solenne Volant & Maryse Brancourt-Hulmel

  2. GCIE-Picardie, INRAE, 80203, Estrées-Mons, UE, France

    Emilie Mignot & Stéphanie Arnoult

  3. GBFOr, INRAE, Ardon, UE, France

    Guillaume Bodineau

  4. Institut Jean-Pierre Bourgin, INRAE, CNRS, Université Paris-Saclay, 78000, Versailles, AgroParisTech, France

    Yves Griveau

Authors
  1. Raphaël Raverdy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emilie Mignot
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stéphanie Arnoult
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laura Fingar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guillaume Bodineau
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yves Griveau
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Solenne Volant
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Maryse Brancourt-Hulmel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MB-H carried out the conception and the design of the study. EM, SA, LF, GB, and RR prepared the material and carried out phenotyping data management and collection. YG realized NIRS data collection and predictions. RR, MB-H, and SV contributed to the analysis of the data. RR wrote the manuscript and all authors commented the manuscript.

Corresponding author

Correspondence to Maryse Brancourt-Hulmel.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raverdy, R., Mignot, E., Arnoult, S. et al. Estimation of Genetic Parameters of Biomass Production and Composition Traits in Miscanthus sinensis Using a Staggered-Start Design. Bioenerg. Res. 15, 735–754 (2022). https://doi.org/10.1007/s12155-022-10459-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12155-022-10459-5

Keywords

Search

Navigation