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Next generation shovelomics: set up a tent and REST

Plant and Soil volume 388, pages 1–20 (2015)Cite this article

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.

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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.

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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
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  2. Norbert Kirchgessner
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  3. Chantal Andrée Le Marié
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  4. Larry Matthew York
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  5. Jonathan P. Lynch
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  6. Andreas Hund
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Correspondence to Andreas Hund.

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Responsible Editor: Peter J. Gregory.

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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)

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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

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

Keywords

  • Root system architecture
  • Maize
  • Automated phenotyping
  • Image processing
  • Heritability