Skip to main content
Log in

Root architectural traits and yield: exploring the relationship in barley breeding trials

  • Published:
Euphytica Aims and scope Submit manuscript

Abstract

Root system architecture is fundamental to resource capture and productivity of cereal crops. Understanding the genetics modulating root development will empower plant breeders to design cultivars with optimal root systems for the target environment. Here, we investigate the genetic association between seminal root traits and yield in elite barley (Hordeum vulgare L.) germplasm. A panel of 216 breeding lines from the Northern Region Barley Breeding program in Australia, genotyped with Diversity Arrays Technology markers, were characterised for seminal root angle and number. A high degree of phenotypic variation was evident in the population, ranging from 12.0° to 89.4° and 4.8 to 6.1 for root angle and number, respectively. A quantitative trait locus for root angle (qRA-5) was detected on chromosome 5H and co-located with the previously described RAQ2. The genetic relationship between seminal root traits and yield for the panel was investigated using root phenotypes and yield data from 20 field trials. Genetic correlations with yield ranged from − 0.21 to 0.36 for root angle and from − 0.20 to 0.25 for root number. The direction and magnitude of the correlations for both root traits varied across the environments, but overall root angle was deemed more strongly associated with yield. Here we provide insight into the root phenotypes of breeding lines and deliver a first look at the genetic relationship between root architectural traits and yield in barley breeding trials.

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

Access this article

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

Similar content being viewed by others

References

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Automatic Control 19:716–723

    Article  Google Scholar 

  • Ali ML, Luetchens J, Nascimento J, Shaver TM, Kruger GR, Lorenz AJ (2015) Genetic variation in seminal and nodal root angle and their association with grain yield of maize under water-stressed field conditions. Plant Soil 397:213–225

    Article  CAS  Google Scholar 

  • Arai-Sanoh Y, Takai T, Yoshinaga S, Nakano H, Kojima M, Sakakibara H, Kondo M, Uga Y (2014) Deep rooting conferred by DEEPER ROOTING 1 enhances rice yield in paddy fields. Sci Rep 4:5563

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Asseng S, Ewert F, Martre P, Rotter RP, Lobell DB, Cammarano D, Kimball BA et al (2015) Rising temperatures reduce global wheat production. Nat Clim Change 5:143–147

    Article  Google Scholar 

  • Bertholdsson N-O, Brantestam AK (2009) A century of Nordic barley breeding—effects on early vigour root and shoot growth, straw length, harvest index and grain weight. Eur J Agron 30:266–274

    Article  Google Scholar 

  • Bingham I, Bengough A (2003) Morphological plasticity of wheat and barley roots in response to spatial variation in soil strength. Plant Soil 250:273–282

    Article  CAS  Google Scholar 

  • Boer M, Cave V, Jansen H, Malosetti M, Mathews K, Murray D, van Eeuwijk F, Welham S (2015) A guide to QTL analysis in Genstat®, 18th edn. VSN International, Hemel Hempstead

    Google Scholar 

  • Borrell AK, Mullet JE, George-Jaeggli B, van Oosterom EJ, Hammer GL, Klein PE, Jordan DR (2014) Drought adaptation of stay-green sorghum is associated with canopy development, leaf anatomy, root growth, and water uptake. J Exp Bot 65:6251–6263

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Butler D, Cullis BR, Gilmour A, Gogel B (2008) ASReml-R reference manual. Department of Primary Industries and Fisheries, Brisbane

    Google Scholar 

  • Chenu K, Cooper M, Hammer GL, Mathews KL, Dreccer MF, Chapman SC (2011) Environment characterization as an aid to wheat improvement: interpreting genotype–environment interactions by modelling water-deficit patterns in North-Eastern Australia. J Exp Bot 62:1743–1755

    Article  CAS  PubMed  Google Scholar 

  • Chloupek O, Forster BP, Thomas WT (2006) The effect of semi-dwarf genes on root system size in field-grown barley. Theor Appl Genet 112:779–786

    Article  CAS  PubMed  Google Scholar 

  • Chloupek O, Dostál V, Středa T, Psota V, Dvořáčková O (2010) Drought tolerance of barley varieties in relation to their root system size. Plant Breed 129:630–636

    Article  Google Scholar 

  • Christopher J, Christopher M, Jennings R, Jones S, Fletcher S, Borrell A, Manschadi A, Jordan D, Mace E, Hammer G (2013) QTL for root angle and number in a population developed from bread wheats (Triticum aestivum) with contrasting adaptation to water-limited environments. Theor Appl Genet 126:1563–1574

    Article  CAS  PubMed  Google Scholar 

  • Core Team R (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  • Covarrubias-Pazaran G (2016) Genome-assisted prediction of quantitative traits using the R package sommer. PLoS One 11:e0156744

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11:381

    Article  Google Scholar 

  • Dai A (2013) Increasing drought under global warming in observations and models. Nat Clim Change 3:52–58

    Article  Google Scholar 

  • Drew M (1975) Comparison of the effects of a localised supply of phosphate, nitrate, ammonium and potassium on the growth of the seminal root system, and the shoot, in barley. New Phytol 75:479–490

    Article  CAS  Google Scholar 

  • Fakrudin B, Kavil S, Girma Y, Arun S, Dadakhalandar D, Gurusiddesh B, Patil A, Thudi M, Bhairappanavar S, Narayana Y (2013) Molecular mapping of genomic regions harbouring QTLs for root and yield traits in sorghum (Sorghum bicolor L. Moench). Physiol Mol Biol Plants 19:409–419

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Forster BP, Franckowiak JD, Lundqvist U, Lyon J, Pitkethly I, Thomas WTB (2007) The barley phytomer. Ann Bot 100:725–733

    Article  PubMed  PubMed Central  Google Scholar 

  • Gilliham M, Able JA, Roy SJ (2017) Translating knowledge about abiotic stress tolerance to breeding programmes. Plant J 90:898–917

    Article  CAS  PubMed  Google Scholar 

  • Gilmour AR, Cullis BR, Verbyla AP (1997) Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat 3:269–293

    Article  Google Scholar 

  • Giuliani S, Sanguineti MC, Tuberosa R, Bellotti M, Salvi S, Landi P (2005) Root-ABA1, a major constitutive QTL, affects maize root architecture and leaf ABA concentration at different water regimes. J Exp Bot 56:3061–3070

    Article  CAS  PubMed  Google Scholar 

  • Grando S, Ceccarelli S (1995) Seminal root morphology and coleoptile length in wild (Hordeum vulgare ssp. Spontaneum) and cultivated (Hordeum vulgare ssp. vulgare). Euphytica 86:73–80

    Article  Google Scholar 

  • Hastie T, Mazumder R, Lee JD, Zadeh R (2015) Matrix completion and low-rank svd via fast alternating least squares. J Mach Learn Res 16:3367–3402

    PubMed  PubMed Central  Google Scholar 

  • Jordan DR, Mace ES, Cruickshank AW, Hunt CH, Henzell RG (2011) Exploring and exploiting genetic variation from unadapted sorghum germplasm in a breeding program. Crop Sci 51:1444–1457

    Article  Google Scholar 

  • Kelly AM, Smith AB, Eccleston JA, Cullis BR (2007) The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials. Crop Sci 47:1063–1070

    Article  Google Scholar 

  • Landi P, Sanguineti M, Liu C, Li Y, Wang T, Giuliani S, Bellotti M, Salvi S, Tuberosa R (2007) Root-ABA1 QTL affects root lodging, grain yield, and other agronomic traits in maize grown under well-watered and water-stressed conditions. J Exp Bot 58:319–326

    Article  CAS  PubMed  Google Scholar 

  • Landi P, Giuliani S, Salvi S, Ferri M, Tuberosa R, Sanguineti MC (2010) Characterization of root-yield-1.06, a major constitutive QTL for root and agronomic traits in maize across water regimes. J Exp Bot 61:3553–3562

    Article  CAS  PubMed  Google Scholar 

  • Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics 28:2397–2399

    Article  CAS  PubMed  Google Scholar 

  • Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333:616–620

    Article  CAS  PubMed  Google Scholar 

  • Lobell DB, Hammer GL, Chenu K, Zheng B, McLean G, Chapman SC (2015) The shifting influence of drought and heat stress for crops in northeast Australia. Global Change Biol 21(21):4115–4127

    Article  Google Scholar 

  • Lynch J (1995) Root architecture and plant productivity. Plant Physiol 109:7–13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lynch JP, Chimungu JG, Brown KM (2014) Root anatomical phenes associated with water acquisition from drying soil: targets for crop improvement. J Exp Bot 65:6155–6166

    Article  CAS  PubMed  Google Scholar 

  • Mace ES, Rami JF, Bouchet S, Klein PE, Klein RR, Kilian A, Wenzl P, Xia L, Halloran K, Jordan DR (2009) A consensus genetic map of sorghum that integrates multiple component maps and high-throughput diversity array technology (DArT) markers. BMC Plant Biol 9:13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mace ES, Singh V, Van Oosterom EJ, Hammer GL, Hunt CH, Jordan DR (2012) QTL for nodal root angle in sorghum (Sorghum bicolor L. Moench) co-locate with QTL for traits associated with drought adaptation. Theor Appl Genet 124:97–109

    Article  CAS  PubMed  Google Scholar 

  • Manschadi AM, Christopher J, Devoil P, Hammer GL (2006) The role of root architectural traits in adaptation of wheat to water-limited environments. Funct Plant Biol 33:823–837

    Article  CAS  PubMed  Google Scholar 

  • Manschadi AM, Hammer GL, Christopher JT, deVoil P (2008) Genotypic variation in seedling root architectural traits and implications for drought adaptation in wheat (Triticum aestivum L.). Plant Soil 303:115–129

    Article  CAS  Google Scholar 

  • Manschadi AM, Christopher JT, Hammer GL, Devoil P (2010) Experimental and modelling studies of drought-adaptive root architectural traits in wheat (Triticum aestivum L.). Plant Biosyst 144:458–462

    Article  Google Scholar 

  • Nicholls N, Drosdowsky W, Lavery B (1997) Australian rainfall variability and change. Weather 52:66–72

    Article  Google Scholar 

  • Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genet 2:e190

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Potgieter AB, Hammer GL, Butler D (2002) Spatial and temporal patterns in Australian wheat yield and their relationship with ENSO. Aust J Agric Res 53:77–89

    Article  Google Scholar 

  • Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909

    Article  CAS  PubMed  Google Scholar 

  • Rich SM, Watt M (2013) Soil conditions and cereal root system architecture: review and considerations for linking Darwin and Weaver. J Exp Bot 64:1193–1208

    Article  CAS  PubMed  Google Scholar 

  • Richard C, Hickey L, Fletcher S, Jennings R, Chenu K, Christopher J (2015) High-throughput phenotyping of seminal root traits in wheat. Plant Methods 11:13

    Article  PubMed  PubMed Central  Google Scholar 

  • Richards R, Passioura J (1989) A breeding program to reduce the diameter of the major xylem vessel in the seminal roots of wheat and its effect on grain yield in rain-fed environments. Crop Pasture Sci 40:943–950

    Article  Google Scholar 

  • Richards RA, Rebetzke GJ, Condon AG, van Herwaarden AF (2002) Breeding opportunities for increasing the efficiency of water use and crop yield in temperate cereals. Crop Sci 42:111–121

    Article  PubMed  Google Scholar 

  • Robinson H, Hickey L, Richard C, Mace E, Kelly A, Borrell A, Franckowiak J, Fox G (2016) Genomic regions influencing seminal root traits in barley. Plant Genome 9:1–13

    Article  Google Scholar 

  • Smith A, Cullis B, Thompson R (2001) Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57:1138–1147

    Article  CAS  PubMed  Google Scholar 

  • Smith AB, Ganesalingam A, Kuchel H, Cullis BR (2015) Factor analytic mixed models for the provision of grower information from national crop variety testing programs. Theor Appl Genet 128:55–72

    Article  PubMed  Google Scholar 

  • Stanca AM, Gianinetti A, Rizza F, Terzi V (2016) Barley: an overview of a versatile cereal grain with many food and feed uses. In: Wrigly C (ed) Encyclopedia of food grains, 2nd edn. Academic Press, Oxford, pp 147–152

    Chapter  Google Scholar 

  • Svačina P, Středa T, Chloupek O (2014) Uncommon selection by root system size increases barley yield. Agron Sustain Dev 34:545–551

    Article  Google Scholar 

  • Tilman D, Balzer C, Hill J, Befort BL (2011) Global food demand and the sustainable intensification of agriculture. Proc Natl Acad Sci USA 108:20260–20264

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tuberosa R, Salvi S, Sanguineti MC, Landi P, Maccaferri M, Conti S (2002) Mapping QTLs regulating morpho-physiological traits and yield: case studies, shortcomings and perspectives in drought-stressed maize. Ann Bot 89:941–963

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Uga Y, Sugimoto K, Ogawa S, Rane J, Ishitani M, Hara N, Kitomi Y, Inukai Y, Ono K, Kanno N, Inoue H, Takehisa H, Motoyama R, Nagamura Y, Wu J, Matsumoto T, Takai T, Okuno K, Yano M (2013) Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Nat Genet 45:1097–1102

    Article  CAS  PubMed  Google Scholar 

  • Voorrips RE (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93:77–78

    Article  CAS  PubMed  Google Scholar 

  • Wang X, Mace ES, Platz GJ, Hunt CH, Hickey LT, Franckowiak JD, Jordan DR (2015) Spot form of net blotch in barley is under complex genetic contol. Theor Appl Genet 128:489–499

    Article  CAS  PubMed  Google Scholar 

  • Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208

    Article  CAS  PubMed  Google Scholar 

  • Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhou Y, Dong G, Tao Y, Chen C, Yang B, Wu Y, Yang Z, Liang G, Wang B, Wang Y (2016) Mapping quantitative trait loci associated with root traits using sequencing-based genotyping chromosome segment substitution lines derived from 9311 and nipponbare in eice (Oryza sativa L.). PLoS One 11:e0151796

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ziems LA, Hickey LT, Hunt CH, Mace ES, Platz GJ, Franckowiak JD, Jordan DR (2014) Association mapping of resistance to Puccinia hordei in Australian barley breeding germplasm. Theor Appl Genet 127:1199–1212

    Article  CAS  PubMed  Google Scholar 

  • Ziems LA, Franckowiak JD, Platz GJ, Mace ES, Park RF, Singh D, Jordan DR, Hickey LT (2017) Investigating successive Australian barley breeding populations for stable resistance to leaf rust. Theor Appl Genet 130:2463–2477

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the University of Queensland, Queensland Alliance for Agriculture and Food Innovation and Grains Research and Development Corporation of Australia, through a PhD scholarship (GRS10940) for Hannah Robinson.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hannah Robinson.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 2961 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Robinson, H., Kelly, A., Fox, G. et al. Root architectural traits and yield: exploring the relationship in barley breeding trials. Euphytica 214, 151 (2018). https://doi.org/10.1007/s10681-018-2219-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10681-018-2219-y

Keywords

Navigation