J Integr Plant Biol. ›› 2014, Vol. 56 ›› Issue (5): 470-479.DOI: 10.1111/jipb.12114

• Research Articles • Previous Articles     Next Articles

Wheat genotypic variability in grain yield and carbon isotope discrimination under Mediterranean conditions assessed by spectral reflectance

Gustavo A. Lobos1, Iván Matus2, Alejandra Rodriguez2, Sebastián Romero-Bravo1, José Luis Araus3 and Alejandro del Pozo1*   

  1. 1Plant Breeding and Phenomic Center, Faculty of Agricultural Sciences, University of Talca, Talca, Chile
    2CRI-Quilamapu, Agricultural Research Institute, Chillán, Chile
    3Plant Physiology Department, Faculty of Biology, University of Barcelona, Barcelona, Spain
  • Received:2013-04-30 Accepted:2013-09-26 Published:2014-05-01
  • About author:*Correspondence: E-mail: adelpozo@utalca.cl

Abstract:

A collection of 368 advanced lines and cultivars of spring wheat (Triticum aestivum L.) from Chile, Uruguay, and CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo), with good agronomic characteristics were evaluated under the Mediterranean conditions of central Chile. Three different water regimes were assayed: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation around booting), and full irrigation (FI; four irrigations: at tillering, flag leaf appearance, heading, and middle grain filling). Traits evaluated were grain yield (GY), agronomical yield components, days from sowing to heading, carbon isotope discrimination (Δ13C) in kernels, and canopy spectral reflectance. Correlation analyses were performed for 70 spectral reflectance indices (SRI) and the other traits evaluated in the three trials. GY and Δ13C were the traits best correlated with SRI, particularly when these indices were measured during grain filling. However, only GY could be predicted using a single regression, with Normalized Difference Moisture Index (NDMI2: 2,200; 1,100) having the best fit to the data for the three trials. For Δ13C, only individual regressions could be forecast under FI (r2: 0.25–0.37) and MWS (r2: 0.45–0.59) but not under SWS (r2: 0.03–0.09). NIR-based SRI proved to be better predictors than those that combine visible and NIR wavelengths.

Key words: Breeding, drought, dry matter index, normalized difference moisture index, vegetative index, water index

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