Wheat genotypic variability in grain yield and carbon isotope discrimination under Mediterranean conditions assessed by spectral reflectance. Issue 5 (4th February 2014)

• Record Type: Journal Article
• Authors: Lobos, Gustavo A.; Matus, Iván; Rodriguez, Alejandra; Romero‐Bravo, Sebastián; Araus, José Luis; del Pozo, Alejandro
• Is Part Of: Journal of integrative plant biology. Volume 56:Issue 5(2014)
• Page Start: 470
• Page End: 479
• Publication Date: 2014-02-04
• Subjects: Plants -- Periodicals; Plants -- China -- Periodicals; Electronic journals; 580.5
• DOI: 10.1111/jipb.12114
• Languages: English
• ISSNs: 1672-9072

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <sec id="jipb12114-sec-0001" sec-type="section"> <p>A collection of 368 advanced lines and cultivars of spring wheat (<italic>Triticum aestivum</italic> 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 (Δ<sup>13</sup>C) 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 Δ<sup>13</sup>C 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 Δ<sup>13</sup>C, only individual regressions could be forecast under FI (<italic>r</italic><sup>2</sup>: 0.25–0.37) and MWS (<italic>r</italic><sup>2</sup>: 0.45–0.59) but not under SWS (<italic>r</italic><sup>2</sup>: 0.03–0.09). NIR‐based SRI proved to be better predictors than those that combine visible and NIR wavelengths.</p> </sec> </abstract>