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Title: High-fidelity detection of crop biomass quantitative trait loci from low-cost imaging in the field

Abstract

Field-based, rapid, and nondestructive techniques for assessing plant productivity are needed to accelerate the discovery of genotype-to-phenotype relationships in next-generation biomass grass crops. The use of hemispherical imaging and light attenuation modeling was evaluated against destructive harvest measures with respect to their ability to accurately capture phenotypic and genotypic relationships in a field-grown grass crop. Plant area index (PAI) estimated from below-canopy hemispherical images, as well as a suite of thirteen traits assessed by manual destructive harvests, were measured in a Setaria recombinant inbred line mapping population segregating for aboveground productivity and architecture. A significant correlation was observed between PAI and biomass production across the population at maturity (r2 = .60), as well as for select diverse genotypes sampled repeatedly over the growing season (r2 = .79). Twenty-seven quantitative trait loci (QTL) were detected for manually collected traits associated with biomass production. Of these, twenty-one were found in four clusters of colocalized QTL. Analysis of image-based estimates of PAI successfully identified all four QTL hot spots for biomass production. QTL for PAI had greater overlap with those detected for traits associated with biomass production than with those for plant architecture and biomass partitioning. Hemispherical imaging is an affordable and scalablemore » method, which demonstrates how high-throughput phenotyping can identify QTL related to biomass production of field trials in place of destructive harvests that are labor, time, and material intensive.« less

Authors:
 [1];  [1];  [2];  [1];  [1];  [3];  [2];  [1]
  1. University of Illinois at Urbana-Champaign, Urbana IL USA
  2. Donald Danforth Plant Science Center, St. Louis MO USA
  3. USDA-ARS, Donald Danforth Plant Science Center, St. Louis MO USA
Publication Date:
Research Org.:
Donald Danforth Plant Science Center, St. Louis, MO (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1423165
Alternate Identifier(s):
OSTI ID: 1423169; OSTI ID: 1530865
Grant/Contract Number:  
SC0008769; SC0018277
Resource Type:
Published Article
Journal Name:
Plant Direct
Additional Journal Information:
Journal Name: Plant Direct Journal Volume: 2 Journal Issue: 2; Journal ID: ISSN 2475-4455
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United Kingdom
Language:
English
Subject:
09 BIOMASS FUELS; crop production; hemispherical photographs; high-throughput phenotyping; Leaf Area Index; setaria

Citation Formats

Banan, Darshi, Paul, Rachel E., Feldman, Max J., Holmes, Mark W., Schlake, Hannah, Baxter, Ivan, Jiang, Hui, and Leakey, Andrew D. B. High-fidelity detection of crop biomass quantitative trait loci from low-cost imaging in the field. United Kingdom: N. p., 2018. Web. doi:10.1002/pld3.41.
Banan, Darshi, Paul, Rachel E., Feldman, Max J., Holmes, Mark W., Schlake, Hannah, Baxter, Ivan, Jiang, Hui, & Leakey, Andrew D. B. High-fidelity detection of crop biomass quantitative trait loci from low-cost imaging in the field. United Kingdom. doi:10.1002/pld3.41.
Banan, Darshi, Paul, Rachel E., Feldman, Max J., Holmes, Mark W., Schlake, Hannah, Baxter, Ivan, Jiang, Hui, and Leakey, Andrew D. B. Thu . "High-fidelity detection of crop biomass quantitative trait loci from low-cost imaging in the field". United Kingdom. doi:10.1002/pld3.41.
@article{osti_1423165,
title = {High-fidelity detection of crop biomass quantitative trait loci from low-cost imaging in the field},
author = {Banan, Darshi and Paul, Rachel E. and Feldman, Max J. and Holmes, Mark W. and Schlake, Hannah and Baxter, Ivan and Jiang, Hui and Leakey, Andrew D. B.},
abstractNote = {Field-based, rapid, and nondestructive techniques for assessing plant productivity are needed to accelerate the discovery of genotype-to-phenotype relationships in next-generation biomass grass crops. The use of hemispherical imaging and light attenuation modeling was evaluated against destructive harvest measures with respect to their ability to accurately capture phenotypic and genotypic relationships in a field-grown grass crop. Plant area index (PAI) estimated from below-canopy hemispherical images, as well as a suite of thirteen traits assessed by manual destructive harvests, were measured in a Setaria recombinant inbred line mapping population segregating for aboveground productivity and architecture. A significant correlation was observed between PAI and biomass production across the population at maturity (r2 = .60), as well as for select diverse genotypes sampled repeatedly over the growing season (r2 = .79). Twenty-seven quantitative trait loci (QTL) were detected for manually collected traits associated with biomass production. Of these, twenty-one were found in four clusters of colocalized QTL. Analysis of image-based estimates of PAI successfully identified all four QTL hot spots for biomass production. QTL for PAI had greater overlap with those detected for traits associated with biomass production than with those for plant architecture and biomass partitioning. Hemispherical imaging is an affordable and scalable method, which demonstrates how high-throughput phenotyping can identify QTL related to biomass production of field trials in place of destructive harvests that are labor, time, and material intensive.},
doi = {10.1002/pld3.41},
journal = {Plant Direct},
number = 2,
volume = 2,
place = {United Kingdom},
year = {2018},
month = {2}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1002/pld3.41

Citation Metrics:
Cited by: 2 works
Citation information provided by
Web of Science

Figures / Tables:

Figure 1 Figure 1: Customized hemispherical imaging system application to high-throughput phenotyping of aboveground biomass production. (a), A hemispherical lens (H) fitted on a GoPro Hero3 + digital camera (C) and mounted on a self-leveling gimbal (G). The camera unit had maximum dimensions of 5.89493.7 cm, and the full system was 11X15X13.3more » cm. (b), The system was used to capture fully hemispherical images of a plant canopy. (c), Images were thresholded for analysis and estimation of Plant Area Index (PAI) using Delta-T HemiView software. (d), These estimates were correlated with total biomass (filled symbols, solid line) and compared to that between Leaf Area Index estimated from destructive harvest and total biomass (open symbols, dashed line). Measurements made on parent lines A.10, B.100 and phenotypically intermediate RIL#161 together represent a diversity of growth habit and morphology seen across the population. Symbols correspond to single plots from which all images and measurements were collected 38, 44, 52, and 60 days after sowing. Correlation r2 values are reported for both measurements« less

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Works referenced in this record:

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journal, June 2017


Future Scenarios for Plant Phenotyping
journal, April 2013


QTL mapping for leaf senescence-related traits in common wheat under limited and full irrigation
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Field-Based High-Throughput Plant Phenotyping Reveals the Temporal Patterns of Quantitative Trait Loci Associated with Stress-Responsive Traits in Cotton
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A High-Throughput, Field-Based Phenotyping Technology for Tall Biomass Crops
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Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding
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Setaria viridis and Setaria italica, model genetic systems for the Panicoid grasses
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Association Mapping: Critical Considerations Shift from Genotyping to Experimental Design
journal, August 2009


Community Resources and Strategies for Association Mapping in Sorghum
journal, January 2008


Data from: High fidelity detection of crop biomass QTL from low-cost imaging in the field
dataset, January 2019


    Works referencing / citing this record:

    QTL mapping for leaf senescence-related traits in common wheat under limited and full irrigation
    journal, November 2014


    Field-based phenomics for plant genetics research
    journal, July 2012


    Phenomics – technologies to relieve the phenotyping bottleneck
    journal, December 2011


    Field high-throughput phenotyping: the new crop breeding frontier
    journal, January 2014


    Reference genome sequence of the model plant Setaria
    journal, May 2012

    • Bennetzen, Jeffrey L.; Schmutz, Jeremy; Wang, Hao
    • Nature Biotechnology, Vol. 30, Issue 6
    • DOI: 10.1038/nbt.2196

    Impacts of a 32-billion-gallon bioenergy landscape on land and fossil fuel use in the US
    journal, January 2016


    Crop genomics: advances and applications
    journal, December 2011

    • Morrell, Peter L.; Buckler, Edward S.; Ross-Ibarra, Jeffrey
    • Nature Reviews Genetics, Vol. 13, Issue 2
    • DOI: 10.1038/nrg3097

    Precision phenotyping of biomass accumulation in triticale reveals temporal genetic patterns of regulation
    journal, August 2013

    • Busemeyer, Lucas; Ruckelshausen, Arno; Möller, Kim
    • Scientific Reports, Vol. 3, Issue 1
    • DOI: 10.1038/srep02442

    Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice
    journal, February 2017

    • Tanger, Paul; Klassen, Stephen; Mojica, Julius P.
    • Scientific Reports, Vol. 7, Issue 1
    • DOI: 10.1038/srep42839

    Global food demand and the sustainable intensification of agriculture
    journal, November 2011

    • Tilman, D.; Balzer, C.; Hill, J.
    • Proceedings of the National Academy of Sciences, Vol. 108, Issue 50
    • DOI: 10.1073/pnas.1116437108

    3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture
    journal, April 2013

    • Topp, C. N.; Iyer-Pascuzzi, A. S.; Anderson, J. T.
    • Proceedings of the National Academy of Sciences, Vol. 110, Issue 18
    • DOI: 10.1073/pnas.1304354110

    Characterizing plant canopies with hemispherical photographs
    journal, January 1990


    R/qtl: QTL mapping in experimental crosses
    journal, May 2003


    Setaria viridis and Setaria italica, model genetic systems for the Panicoid grasses
    journal, March 2011

    • Li, P.; Brutnell, T. P.
    • Journal of Experimental Botany, Vol. 62, Issue 9
    • DOI: 10.1093/jxb/err096

    Foxtail Millet: A Sequence-Driven Grass Model System: Figure 1.
    journal, January 2009

    • Doust, Andrew N.; Kellogg, Elizabeth A.; Devos, Katrien M.
    • Plant Physiology, Vol. 149, Issue 1
    • DOI: 10.1104/pp.108.129627

    A High-Throughput, Field-Based Phenotyping Technology for Tall Biomass Crops
    journal, June 2017

    • Salas Fernandez, Maria G.; Bao, Yin; Tang, Lie
    • Plant Physiology, Vol. 174, Issue 4
    • DOI: 10.1104/pp.17.00707

    Association Mapping: Critical Considerations Shift from Genotyping to Experimental Design
    journal, August 2009


    Setaria viridis : A Model for C 4 Photosynthesis
    journal, August 2010

    • Brutnell, Thomas P.; Wang, Lin; Swartwood, Kerry
    • The Plant Cell, Vol. 22, Issue 8
    • DOI: 10.1105/tpc.110.075309

    Conventional digital cameras as a tool for assessing leaf area index and biomass for cereal breeding: Conventional digital cameras for cereal breeding
    journal, December 2013

    • Casadesús, Jaume; Villegas, Dolors
    • Journal of Integrative Plant Biology, Vol. 56, Issue 1
    • DOI: 10.1111/jipb.12117

    Future Scenarios for Plant Phenotyping
    journal, April 2013


    Genotype-by-Environment Interaction and Plasticity: Exploring Genomic Responses of Plants to the Abiotic Environment
    journal, November 2013


    Time dependent genetic analysis links field and controlled environment phenotypes in the model C4 grass Setaria
    journal, June 2017


    Yield Trends Are Insufficient to Double Global Crop Production by 2050
    journal, June 2013


    Development and Genetic Control of Plant Architecture and Biomass in the Panicoid Grass, Setaria
    journal, March 2016


    Field-Based High-Throughput Plant Phenotyping Reveals the Temporal Patterns of Quantitative Trait Loci Associated with Stress-Responsive Traits in Cotton
    journal, January 2016

    • Pauli, Duke; Andrade-Sanchez, Pedro; Carmo-Silva, A. Elizabete
    • G3: Genes|Genomes|Genetics, Vol. 6, Issue 4
    • DOI: 10.1534/g3.115.023515

    Community Resources and Strategies for Association Mapping in Sorghum
    journal, January 2008


    Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding
    journal, September 2014


    A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization
    journal, May 2016

    • Vergara-Díaz, Omar; Zaman-Allah, Mainassara A.; Masuka, Benhildah
    • Frontiers in Plant Science, Vol. 7
    • DOI: 10.3389/fpls.2016.00666

    Genotyping-by-Sequencing for Plant Breeding and Genetics
    journal, January 2012


    Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum
    journal, September 2018

    • Young, Sierra N.; Kayacan, Erkan; Peschel, Joshua M.
    • Precision Agriculture, Vol. 20, Issue 4
    • DOI: 10.1007/s11119-018-9601-6

    Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum
    journal, September 2018

    • Young, Sierra N.; Kayacan, Erkan; Peschel, Joshua M.
    • Precision Agriculture, Vol. 20, Issue 4
    • DOI: 10.1007/s11119-018-9601-6

      Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.