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Spectral assessment of drought tolerance indices and grain yield in advanced spring wheat lines grown under full and limited water irrigation

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  • El-Hendawy, Salah E.
  • Hassan, Wael M.
  • Al-Suhaibani, Nasser A.
  • Schmidhalter, Urs

Abstract

Because wheat varieties exhibit a high genotype×environment interaction, several drought tolerance indices (DTIs) have been developed to assist breeders in selecting genotypes with good performance under contrasting water conditions. We compared the relative yield of advanced breeding wheat lines under both well-watered and limited water irrigation conditions using different DTIs and evaluated how spectral reflectance indices (SRIs), as rapid and non-destructive tools, can effectively monitor DTIs and grain yield. Sixty-five recombinant inbred lines (RILs) developed from a cross between drought-tolerant (Sakha 93) and drought-sensitive (Sakha 61) genotypes were subjected to full irrigation (FI) and limited water irrigation (LWI) in the 2014 (F6), 2015 (F7), and 2016 (F8) growing seasons. Eight vegetation- and water-SRIs calculated from canopy reflectance under FI and LWI, and taken at the heading and middle grain filling stages, were related to the DTIs and grain yield. We found that the yield performance of the RILs was not consistent across the two water regimes. Selection based on the DTIs, the stress susceptibility index and the tolerance index failed to identify RILs that had very low yields under both treatments. However, the mean productivity index (MPI) and the geometric mean productivity index (GMP) enabled us to identify RILs that produced desirable yields under both full and limited irrigation, and these drought tolerance indices further exhibited a high heritability. Across the three years of investigation and at the heading and middle grain filling stages, these DTIs were best described either by the vegetation-based dry matter content index (DMCI) or the water-based normalized multi-band drought index (NDMI), or a combination of both. In conclusion, our results demonstrate that a combination of near infrared (NIR) and shortwave infrared (SWIR)-based SRIs can be used as a fast and low-cost predictor for selecting wheat genotypes with superior yield under different water regimes.

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  • El-Hendawy, Salah E. & Hassan, Wael M. & Al-Suhaibani, Nasser A. & Schmidhalter, Urs, 2017. "Spectral assessment of drought tolerance indices and grain yield in advanced spring wheat lines grown under full and limited water irrigation," Agricultural Water Management, Elsevier, vol. 182(C), pages 1-12.
    • Handle: RePEc:eee:agiwat:v:182:y:2017:i:c:p:1-12
    DOI: 10.1016/j.agwat.2016.12.003
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    References listed on IDEAS

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    1. El-Hendawy, Salah E. & Schmidhalter, Urs, 2010. "Optimal coupling combinations between irrigation frequency and rate for drip-irrigated maize grown on sandy soil," Agricultural Water Management, Elsevier, vol. 97(3), pages 439-448, March.
    2. Elazab, Abdelhalim & Bort, Jordi & Zhou, Bangwei & Serret, María Dolors & Nieto-Taladriz, María Teresa & Araus, José Luis, 2015. "The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions," Agricultural Water Management, Elsevier, vol. 158(C), pages 196-208.
    3. El-Hendawy, Salah E. & Kotab, Maher A. & Al-Suhaibani, Nasser A. & Schmidhalter, Urs, 2014. "Optimal coupling combinations between the irrigation rate and glycinebetaine levels for improving yield and water use efficiency of drip-irrigated maize grown under arid conditions," Agricultural Water Management, Elsevier, vol. 140(C), pages 69-78.
    4. Payero, José O. & Tarkalson, David D. & Irmak, Suat & Davison, Don & Petersen, James L., 2008. "Effect of irrigation amounts applied with subsurface drip irrigation on corn evapotranspiration, yield, water use efficiency, and dry matter production in a semiarid climate," Agricultural Water Management, Elsevier, vol. 95(8), pages 895-908, August.
    5. Perry, Chris & Steduto, Pasquale & Allen, Richard. G. & Burt, Charles M., 2009. "Increasing productivity in irrigated agriculture: Agronomic constraints and hydrological realities," Agricultural Water Management, Elsevier, vol. 96(11), pages 1517-1524, November.
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    1. El-Hendawy, Salah E. & Al-Suhaibani, Nasser A. & Elsayed, Salah & Hassan, Wael M. & Dewir, Yaser Hassan & Refay, Yahya & Abdella, Kamel A., 2019. "Potential of the existing and novel spectral reflectance indices for estimating the leaf water status and grain yield of spring wheat exposed to different irrigation rates," Agricultural Water Management, Elsevier, vol. 217(C), pages 356-373.
    2. Peng, Zhigong & Lin, Shaozhe & Zhang, Baozhong & Wei, Zheng & Liu, Lu & Han, Nana & Cai, Jiabing & Chen, He, 2020. "Winter Wheat Canopy Water Content Monitoring Based on Spectral Transforms and “Three-edge” Parameters," Agricultural Water Management, Elsevier, vol. 240(C).
    3. Mohammed Mohi-Ud-Din & Md. Alamgir Hossain & Md. Motiar Rohman & Md. Nesar Uddin & Md. Sabibul Haque & Eldessoky S. Dessoky & Mohammed Alqurashi & Salman Aloufi, 2022. "Assessment of Genetic Diversity of Bread Wheat Genotypes for Drought Tolerance Using Canopy Reflectance-Based Phenotyping and SSR Marker-Based Genotyping," Sustainability, MDPI, vol. 14(16), pages 1-19, August.

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