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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Plant Sci. | doi: 10.3389/fpls.2019.00394

High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat

  • 1Kansas State University, United States
  • 2The International Maize and Wheat Improvement Center (CIMMYT), India
  • 3University of Swabi, Pakistan
  • 4International Maize and Wheat Improvement Center (CIMMYT), Pakistan
  • 5International Maize and Wheat Improvement Center (Mexico), Mexico

Novel high-throughput phenotyping (HTP) approaches are needed to advance the understanding of genotype-to-phenotype and accelerate plant breeding. The first generation of HTP has examined simple spectral reflectance traits from images and sensors but is limited in advancing our understanding of crop development and architecture. Lodging is a complex trait that significantly impacts yield and quality in many crops including wheat. Conventional visual assessment methods for lodging are time-consuming, relatively low-throughput, and subjective, limiting phenotyping accuracy and population sizes in breeding and genetics studies. Here we demonstrate the considerable power of unmanned aerial systems (UAS) or drone-based phenotyping as a high-throughput alternative to visual assessments for the complex phenological trait of lodging, which significantly impacts yield and quality in many crops including wheat. We tested and validated quantitative assessment of lodging on 2,640 wheat breeding plots over the course of two years using differential digital elevation models from UAS. High correlations of digital measures of lodging to visual estimates and equivalent broad-sense heritability demonstrate this approach is amenable for reproducible assessment of lodging in large breeding nurseries. Using these high-throughput measures to assess the underlying genetic architecture of lodging in wheat, we applied genome-wide association analysis and identified a key genomic region on chromosome 2A, consistent across digital and visual scores of lodging. However, these associations accounted for a very minor portion of the total phenotypic variance. We therefore investigated whole genome prediction models and found high prediction accuracies across populations and environments. This adequately accounted for the highly polygenic genetic architecture of numerous small effect loci, consistent with the previously described complex genetic architecture of lodging in wheat. Our study provides a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to uncover the genetic factors and increase the rate of gain for complex traits in crop breeding.

Keywords: wheat, High throughput phenotyping, Lodging, breeding & genomics, GWAS - genome-wide association study, Triticum aestivum, Genomic prediction, unmanned aerial systems (UAS)

Received: 05 Jul 2018; Accepted: 14 Mar 2019.

Edited by:

Hiroyoshi Iwata, The University of Tokyo, Japan

Reviewed by:

Maria Balota, Virginia Tech, United States
Sebastian Michel, University of Natural Resources and Life Sciences Vienna, Austria  

Copyright: © 2019 Singh, Wang, Kumar, Gao, Noor, Imtiaz, Singh and Poland. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Jesse Poland, Kansas State University, Manhattan, United States,