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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.2019.01537

Comparative Performance of Spectral Reflectance Indices and Multivariate Modeling for Assessing Agronomic Parameters in Advanced Spring Wheat Lines under Two Contrasting Irrigation Regimes

  • 1Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Saudi Arabia
  • 2Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Saudi Arabia
  • 3College of Science and Humanities, Shaqra University, Saudi Arabia
  • 4Faculty of Agriculture, Suez Canal University, Egypt
  • 5Environmental Studies and Research Institute, University of Sadat City, Egypt
  • 6Department of Plant Sciences, Technical University of Munich, Germany

The incorporation of non-destructive and cost-effective tools in genetic drought studies in combination with reliable indirect screening criteria that exhibit high heritability and genetic correlations will be critical for addressing the water deficit challenges of the agricultural sector under arid conditions and ensuring the success of genotype development. In this study, proximal spectral reflectance data were used to assess three destructive parameters [dry weight (DW) and water content (WC) of the aboveground biomass and grain yield (GY)] in 30 recombinant F7 and F8 inbred lines (RILs) grown under full (FL) and limited (LM) irrigation regimes. The utility of different groups of spectral reflectance indices (SRIs) as an indirect assessment tool was tested based on heritability and genetic correlations. The performance of the SRIs and different models of partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) in estimating the destructive parameters was considered. Generally, all groups of SRIs, as well as different models of PLSR and SMLR, generated better estimations for destructive parameters under LM and combined FL+LM than under FL. Even though most of the SRIs exhibited a low association with destructive parameters under FL, they exhibited moderate to high genetic correlations and also had high heritability. The SRIs based on near-infrared (NIR)/visible (VIS) and NIR/NIR, especially those developed in this study, spectral band intervals extracted within VIS, red edge, and NIR spectral range, or individual effective wavelengths relevant to green, red, red edge, and middle NIR spectral region, were found to be more effective in estimating the destructive parameters under all conditions. Five models of SMLR and PLSR for each condition explained most of the variation in the three destructive parameters among genotypes. Overall, these results confirmed that application of hyperspectral reflectance sensing in breeding programs is not only important for evaluating a large set of genotypes in an expeditious and cost-effective manner but also could be exploited to develop indirect breeding traits that aid in accelerating the development of genotypes for application under adverse environmental conditions.

Keywords: partial least squares regression, Phenomics, phenotyping, proximal sensing techniques, recombinant inbred lines, Stepwise multiple linear regression, Wavelength band selection

Received: 12 Jun 2019; Accepted: 04 Nov 2019.

Copyright: © 2019 El-Hendawy, Alotaibi, Alsuhaibani, Al-Gaadi, Hassan, Dewir, Emam, Elsayed and Schmidhalter. 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: Prof. Salah E. El-Hendawy, Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh, 11451, Saudi Arabia,