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

Because wheat varieties exhibit a high genotype x 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 (F-6), 2015 (F-7), and 2016 (F-8) 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. (C) 2016 Elsevier B.V. All rights reserved.