Skip to main content
SpringerLink
Log in

Next generation shovelomics: set up a tent and REST

  • Methods Paper
  • Published:
Plant and Soil Aims and scope Submit manuscript

Abstract

Aims

Root system architecture traits (RSAT) are crucial for crop productivity, especially under drought and low soil fertility. The “shovelomics” method of field excavation of mature root crowns followed by manual phenotyping enables a relatively high throughput as needed for breeding and quantitative genetics. We aimed to develop a new sampling protocol in combination with digital imaging and new software.

Methods

Sampled rootstocks were split lengthwise, photographed under controlled illumination in an imaging tent and analysed using Root Estimator for Shovelomics Traits (REST). A set of 33 diverse maize hybrids, grown at 46 and 192 kg N ha−1, was used to evaluate the method and software.

Results

Splitting of the crowns enhanced soil removal and enabled access to occluded traits: REST-derived median gap size correlated negatively (r = −0.62) with lateral root density based on counting. The manually measured root angle correlated with the image-derived root angle (r = 0.89) and the horizontal extension of the root system (r = 0.91). The heritabilities of RSAT ranged from 0.45 to 0.81, comparable to heritabilities of plant height and leaf biomass.

Conclusion

The combination of the novel crown splitting method, combined with imaging under controlled illumination followed by automatic analysis with REST, allowed for higher throughput while maintaining precision. The REST Software is available as supplement.

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
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abendroth LJ, Elmore RW, Boyer MJ, Marlay SK (2011) Corn growth and development. PMR 1009. Iowa State Univ. Extension, Ames

    Google Scholar 

  • Araki H, Hirayama M, Hirasawa H, Iijima M (2000) Which roots penetrate the deepest in rice and maize root systems. Plant Prot Sci 3:281–288

    Article  Google Scholar 

  • Bucksch A, Burridge J, York LM et al (2014) Image-based high-throughput field phenotyping of crop roots. Plant Physiol 166:470–486. doi:10.1104/pp. 114.243519

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Burton AL, Brown KM, Lynch JP (2013) Phenotypic diversity of root anatomical and architectural traits in Zea species. Crop Sci 53:1–15

    Article  Google Scholar 

  • Cai H, Chen F, Mi G et al (2012) Mapping QTLs for root system architecture of maize (Zea mays L.) in the field at different developmental stages. Theor Appl Genet 125:1313–24. doi:10.1007/s00122-012-1915-6

    Article  PubMed  Google Scholar 

  • Campos H, Cooper M, Habben JE et al (2004) Improving drought tolerance in maize: a view from industry. Fields Crop Res 90:19–34

    Article  Google Scholar 

  • Chun L, Mi G, Li J et al (2005) Genetic analysis of maize root characteristics in response to low nitrogen stress. Plant Soil 276:369–382

    Article  CAS  Google Scholar 

  • Coque M, Martin A, Veyrieras JB et al (2008) Genetic variation for N-remobilization and postsilking N-uptake in a set of maize recombinant inbred lines. 3. QTL detection and coincidences. Theor Appl Genet 117:729–747

    Article  CAS  PubMed  Google Scholar 

  • Falconer DS, Mackay TF (1996) Introduction to quantitative genetics, 4th edn. Longman, Harlow

    Google Scholar 

  • Gallais A, Coque M (2005) Genetic variation and selection for nitrogen use efficiency in maize: a synthesis. Maydica 50:531–547

    Google Scholar 

  • Gallais A, Hirel B (2004) An approach to the genetics of nitrogen use efficiency in maize. J Exp Bot 55:295–306

    Article  CAS  PubMed  Google Scholar 

  • Gaudin ACM, McClymont SA, Holmes BM et al (2011) Novel temporal, fine-scale and growth variation phenotypes in roots of adult-stage maize (Zea mays L.) in response to low nitrogen stress. Plant Cell Environ 34:2122–2137

    Article  CAS  PubMed  Google Scholar 

  • Giuliani S, Sanguineti MC, Tuberosa R et al (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 

  • Grieder C, Trachsel S, Hund A (2014) Early vertical distribution of roots and its association with drought tolerance in tropical maize. Plant Soil 377:295–308. doi:10.1007/s11104-013-1997-1

    Article  CAS  Google Scholar 

  • Grift TE, Novais J, Bohn M (2011) High-throughput phenotyping technology for maize roots. Biosyst Eng 110:40–48

    Article  Google Scholar 

  • Guilmour AR, Gogel BJ, Cullis BR, Thompson R (2009) ASReml user guide release 3.0. VSN Int. Ltd, Hemel Hempstead, HP1 1ES, UK

    Google Scholar 

  • Hammer G, Dong Z, McLean G et al (2009) Can changes in canopy and/or root system architecture explain historical maize yield trends in the US corn belt? Crop Sci 49:299–312

    Article  Google Scholar 

  • Hochholdinger F (2009) The maize root system: morphology, anatomy, and genetics. In: Hake SC, Bennetzen JL (eds) Handb. maize Its Biol. Springer New York, New York, pp 145–160

    Chapter  Google Scholar 

  • Hochholdinger F, Tuberosa R (2009) Genetic and genomic dissection of maize root development and architecture. Curr Opin Plant Biol 12:172–177

    Article  CAS  PubMed  Google Scholar 

  • Hund A (2010) Genetic variation in the gravitropic response of maize roots to low temperatures. Plant Roots 4:22–30. doi:10.3117/plantroot.4.22

    Article  Google Scholar 

  • Hund A, Fracheboud Y, Soldati A et al (2004) QTL controlling root and shoot traits of maize seedlings under cold stress. Theor Appl Genet 109:618–29

    Article  CAS  PubMed  Google Scholar 

  • Hund A, Reimer R, Messmer R (2011) A consensus map of QTLs controlling the root length of maize. Plant Soil 344:143–158

    Article  CAS  Google Scholar 

  • Iyer-Pascuzzi AS, Symonova O, Mileyko Y et al (2010) Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems. Plant Physiol 152:1148–1157

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Kahn BA, Stoffella JP (1991) Nodule distribution among root morphological components of field-grown cowpeas. J Am Soc Hortic Sci 116:655–658

    Google Scholar 

  • Ku LX, Sun ZH, Wang CL et al (2012) QTL mapping and epistasis analysis of brace root traits in maize. Mol Breed 30:697–708

    Article  Google Scholar 

  • Kumar B, Abdel Ghani AH, Reyes-Matamoros J et al (2012) Genotypic variation for root architecture traits in seedlings of maize (Zea mays L.) inbred lines. Plant Breed 131:465–478

    Article  Google Scholar 

  • Kutscherea L, Lichtenegger E (1960) Wurzelatlas mitteleuropäischer Ackerunkräuter und Kulturpflanzen. DLG-Verlag, Frankfurt am Main

    Google Scholar 

  • Liedgens M, Soldati A, Stamp P, Richner W (2000) Root development of maize (Zea mays L.) as observed with minirhizotrons in lysimeters. Crop Sci 40:1665–1672

    Article  Google Scholar 

  • Liu J, Li J, Chen F (2008) Mapping QTLs for root traits under different nitrate levels at the seedling stage in maize (Zea mays L.). Plant Soil 305:253–265

    Article  CAS  Google Scholar 

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

    PubMed Central  CAS  PubMed  Google Scholar 

  • Lynch JP (2013) Steep, cheap and deep: an ideotype to optimize water and N acquisition by maize root systems. Ann Bot. doi:10.1093/aob/mcs293

    Google Scholar 

  • Maddonni GA, Otegui E, Andrieu B et al (2002) Maize leaves turn away from neighbors. Plant Physiol 130:1181–1189. doi:10.1104/pp. 009738.nated

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Manavalan LP, Musket T, Nguyen HT (2012) Natural genetic variation for root traits among diversity lines of maize (Zea Mays L.). Maydica 56

  • Moisy F (2006) “Boxcount” (Matlab Central, 2006). http://www.mathworks.com/matlabcentral/fileexchange/13063-boxcount. Accessed 30 May 2013

  • Nielsen KL, Lynch JP, Weiss HN (1997) Fractal geometry of bean root systems: correlations between spatial and fractal dimension. Am J Bot 84:26–33

    Article  CAS  PubMed  Google Scholar 

  • Nielsen KL, Miller CR, Beck D, Lynch JP (1999) Fractal geometry of root systems: field observations of contrasting genotypes of common bean (Phaseolus vulgaris L.) grown under different phosphorus regimes. Plant Soil 206:181–190

    Article  Google Scholar 

  • Otsu N (1979) A threshold selection method from gray-level histogrmas. IEEE Trans Syst Man Cybern 9:62–6

    Article  Google Scholar 

  • Passioura JB (2012) Phenotyping for drought tolerance in grain crops: when is it useful to breeders? Funct Plant Biol 39:851–859

    Article  Google Scholar 

  • Piepho H-P, Möhring J (2007) Computing heritability and selection response from unbalanced plant breeding trials. Genetics 177:1881–1888

    Article  PubMed Central  PubMed  Google Scholar 

  • R Core Team (2013) R: a language and environment for statistical computing

  • Ruta N, Liedgens M, Fracheboud Y (2010) QTLs for the elongation of axile and lateral roots of maize in response to low water potential. Theor Appl Genet 120:621–631

    Article  CAS  PubMed  Google Scholar 

  • Saengwilai P, Tian X, Lynch JP (2014) Low crown root number enhances nitrogen acquisition from low-nitrogen soils in maize. Plant Physiol 166:581–9. doi:10.1104/pp. 113.232603

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Singh V, Oosterom EJ, Jordan DR et al (2010) Morphological and architectural development of root systems in sorghum and maize. Plant Soil 333:287–299

    Article  CAS  Google Scholar 

  • Stoffella PJ, Sandsted RF, Zobel RW, Hymes WL (1979) Root characteristics of black beans. II. Morphological differences among genotypes. Crop Sci 19:826–830

    Article  Google Scholar 

  • Trachsel S, Kaeppler SM, Brown KM, Lynch JP (2011) Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant Soil 341:75–87

    Article  CAS  Google Scholar 

  • Trachsel S, Kaeppler SM, Brown KM, Lynch JP (2013) Maize root growth angles become steeper under low N conditions. Fields Crop Res 140:18–31

    Article  Google Scholar 

  • Vamerali T, Saccomani M, Bona S et al (2003) A comparison of root characteristics in relation to nutrient and water stress in two maize hybrids. Plant Soil 255:157–167

    Article  CAS  Google Scholar 

  • Vargas M, Combs E, Alvarado G et al (2013) META: a suite of SAS programs to analyze multienvironment breeding trials. Agron J 105:11–19

    Article  Google Scholar 

  • Walk TC, Van Erp E, Lynch JP (2004) Modelling applicability of fractal analysis to efficiency of soil exploration by roots. Ann Bot 94:119–28. doi:10.1093/aob/mch116

    Article  PubMed Central  PubMed  Google Scholar 

  • Weaver JE (1925) Investigations on the root habits of plants. Am J Bot 12:502–509

    Article  Google Scholar 

  • Wiesler F, Horst WJ (1994) Root growth and nitrate utilization of maize cultivars under field conditions. Plant Soil 163:267–277

    Article  CAS  Google Scholar 

  • Worku M, Bänziger M, Friesen D, Diallo AO, Horst WJ (2012) Nitrogen efficiency as related to dry matter partitioning and root system size in tropical mid-altitude maize hybrids under different levels of nitrogen stress. Fields Crop Res 130:57–67

  • Wu L, McGechan MB, Watson CA, Baddeley JA (2005) Develping existing plant root system architecture models to meet future agricultural challenges. Adv Agron 85:181–219

    Article  Google Scholar 

  • York LM, Nord EA, Lynch JP (2013) Integration of root phenes for soil resource acquisition. Front Plant Sci 4:1–15. doi:10.3389/fpls.2013.00355

    Article  Google Scholar 

  • Yu G-R, Zhuang J, Nakayama K, Jin Y (2007) Root water uptake and profile soil water as affected by vertical root distribution. Plant Ecol 189:15–30

    Article  Google Scholar 

  • Zhong D, Novais J, Grift TE et al (2009) Maize root complexity analysis using a support vector machine method. Comput Electron Agric 69:46–50

    Article  Google Scholar 

  • Zhu J, Kaeppler SM, Lynch JP (2005a) Mapping of QTLs for lateral root branching and length in maize (Zea mays L.) under differential phosphorus supply. Theor Appl Genet 111:688–95. doi:10.1007/s00122-005-2051-3

    Article  CAS  PubMed  Google Scholar 

  • Zhu J, Kaeppler SM, Lynch JP (2005b) Topsoil foraging and phosphorus acquisition efficiency in maize (Zea mays). Funct Plant Biol 32:749. doi:10.1071/FP05005

    Article  CAS  Google Scholar 

  • Zhu J, Ingram PA, Benfey PN, Elich T (2011) From lab to field, new approaches to phenotyping root system architecture. Curr Opin Plant Biol 14:310–317

    Article  PubMed  Google Scholar 

  • Zobel RW (2011) A developmental genetic basis for defining root classes. Crop Sci 51:1410. doi:10.2135/cropsci2010.11.0652

    Article  Google Scholar 

  • Zobel RW, Waisel Y (2010) A plant root system architectural taxonomy: a framework for root nomenclature. Plant Biosyst 144:507–512. doi:10.1080/11263501003764483

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers for their helpful suggestions and Achim Walter for his support. Thanks for assistance in the field go to Johan Prinsloo and the farmworkers in South Africa. Thanks to Claude Welcker for his assistance in assembling the EURoot maize panel and to Delley Seeds and Plants Ltd. for hybrid production. We kindly thank the donors of the genetic material: Department of Agroenvironmental Science and Technologies (DiSTA), University of Bologna, Italy (RootABA lines); Misión Biológica de Galicia (CSIC), Spain (EP52); Estación Experimental de Aula Dei (CSIC), Spain (EZ47, EZ11A, EZ37); Centro Investigaciones Agrarias de Mabegondo (CIAM), Spain (EC169); Misión Biológica de Galicia (CSIC), Spain, (EP52); University of Hohenheim, Versuchsstation für Pflanzenzüchtung, Germany (UH007, UH250); and INRA CNRS UPS AgroParisTech, France (supply of the remaining, public lines). We thank the Forschungszentrum Jülich GmbH, Germany for the MatLib package and Oliver Dressler for the CAD illustrations. Support for field research in South Africa was provided to Jonathan Lynch by the Howard G. Buffett Foundation. This research received funding from the European Community Seventh Framework Programme FP7-KBBE-2011-5 under grant agreement no.289300 and the Walter Hochstrasser-Stiftung.

Author information

Authors and Affiliations

  1. ETH Zurich, Institute of Agricultural Sciences, 8092, Zurich, Switzerland

    Tino Colombi, Norbert Kirchgessner, Chantal Andrée Le Marié & Andreas Hund

  2. Graduate Program in Ecology, The Pennsylvania State University, University Park, PA, 16802, USA

    Larry Matthew York & Jonathan P. Lynch

  3. Department of Plant Science, The Pennsylvania State University, University Park, PA, 16802, USA

    Larry Matthew York & Jonathan P. Lynch

Authors
  1. Tino Colombi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Norbert Kirchgessner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chantal Andrée Le Marié
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Larry Matthew York
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jonathan P. Lynch
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andreas Hund
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andreas Hund.

Additional information

Responsible Editor: Peter J. Gregory.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Figure S1

Image processing with RootEstimatorForShovelomicsTraits from binary images: Determination of fractal dimensions by stepwise reduction of the grid fineness, here exemplary: a) 512*512 mashes, b) 256*256 mashes, c) 128*128 mashes and d) 64*64 mashes. (TIFF 248 kb)

Figure S2

Scatter plot and Pearson correlation coefficients between genotype means of root top angle (AngRt) and a) root angle of the youngest whorl (AngNo-0), b) the second youngset whorl (AngNo-1) and c) the third youngest whorl (AngNo-2); (**) denotes significant correlations on p-level 0.01, (n.s.) denotes non-siginificant correlations. (TIFF 86 kb)

Figure S3

Scatter plots and Pearson correlation coefficients between genotype means of the area of the convex hull (AcH) and a) nodal root number at the youngest whorl (#NoNo-0) and b) the projected total structure length; (**), (°) denote significant correlations on p-level 0.01 and 0.1 respectively. (TIFF 68 kb)

Figure S4

Scatter plots and Pearson correlation coefficients between genotype means of the leaf fresh weight (FWLf) and a) nodal root number at the youngest whorl (#NoNo-0), b) the area of the convex hull (AcH) and c) the fractal dimension (FD); (**), (*) denote significant correlations on p-level 0.01 and 0.05 respectively, (n.s.) denotes non-siginificant correlations. (TIFF 80 kb)

Figure S5

Scatter plots and Pearson correlation coefficients between genotype means of the a) branching density of lateral roots at 3rd youngest whorl (BDNo-2) and the median gap size and b) the filling factor (Ff) in the convex hull and the median gap size; (*) denote significant correlations on p-level 0.05. (TIFF 72 kb)

Figure S6

Mean root angles of the youngest (AngNo-0), second youngest (AngNo-1) and third youngest (AngNo-2) nodal root whorl of each genotype under high (HN; open circles) or low (LN; cross) nitrogen. (TIFF 136 kb)

ESM 7

(ZIP 26370 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Colombi, T., Kirchgessner, N., Le Marié, C.A. et al. Next generation shovelomics: set up a tent and REST. Plant Soil 388, 1–20 (2015). https://doi.org/10.1007/s11104-015-2379-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11104-015-2379-7

Keywords

Search

Navigation