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• W2897858114 abstract "Foliar reflectance is readily used in evaluating physiological status of agricultural crops and forest stands. However, in the case of forest trees, underlying genetics of foliar spectral reflectance and pigment content have rarely been investigated. We studied a structured population of Scots pine, replicated on two sites, with the selected trees´ pedigree reconstructed via DNA markers. This allowed us to decompose phenotypic variance of pigment and reflectance traits into its causal genetic components, and to estimate narrow-sense heritability (h2). We found statistically significant h2 ranging from 0.07 to 0.22 for most of the established reflectance indices. Additionally, we investigated the profile of heritable variation along the reflectance curve in 1 nm wavelength (WL) bands. We show that the maximum h2 value (0.39; SE 0.13) across the 400 to 2500 nm spectral range corresponds to the red edge inflection point, in this case to 722 nm WL band. Resultant h2 distribution indicates that additive gene effects fluctuate along the reflectance curve. Furthermore, h2 of the most widely used formats of reflectance indices, i.e. the simple ratio and the normalized difference, was estimated for all WL bands combined along the observed reflectance spectrum. The highest h2 estimates for both formats were obtained by combining WL bands of the red edge spectrum. These new genetically driven pigment- and spectral reflectance- based markers (proxies of adaptive traits) may facilitate selection of stress resistant plant genotypes. Recent development of high-resolution spectral sensors carried by airborne and spaceborn devices make foliage spectral traits a viable technology for mass phenotyping in forest trees." @default.
• W2897858114 created "2018-10-26" @default.
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• W2897858114 date "2018-12-01" @default.
• W2897858114 modified "2023-10-17" @default.
• W2897858114 title "Heritable variation in needle spectral reflectance of Scots pine (Pinus sylvestris L.) peaks in red edge" @default.
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• W2897858114 doi "https://doi.org/10.1016/j.rse.2018.10.001" @default.
• W2897858114 hasPublicationYear "2018" @default.
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