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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)
OSTI Identifier:
1423165
Alternate Identifier(s):
OSTI ID: 1423169; OSTI ID: 1530865
Grant/Contract Number:  
DE‐SC0008769; SC0018277; SC0008769
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. https://doi.org/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. https://doi.org/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. https://doi.org/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
https://doi.org/10.1002/pld3.41

Citation Metrics:
Cited by: 2 works
Citation information provided by
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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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