Predicting photosynthetic capacity in tobacco using shortwave infrared spectral reflectance. (18th March 2021)

• Record Type: Journal Article
• Authors: Sexton, Thomas; Sankaran, Sindhuja; Cousins, Asaph B
• Editors: Lawson, Tracy
• Is Part Of: Journal of experimental botany. Volume 72:Number 12(2021)
• Page Start: 4373
• Page End: 4383
• Publication Date: 2021-03-18
• Subjects: Chlorophyll -- hyperspectral reflectance -- nitrogen -- photosynthetic capacity -- partial least squares regression -- shortwave infrared -- Vcmax; Botany -- Periodicals; Botany, Experimental -- Periodicals; Plant physiology -- Periodicals; 580
• DOI: 10.1093/jxb/erab118
• Languages: English
• ISSNs: 0022-0957

Abstract

Abstract : Predictions of photosynthetic capacity made using hyperspectral reflectance in transgenic tobacco genotypes showed that model accuracy was greatest when spectral data were limited to only the shortwave infrared region. Abstract: Plateauing yield and stressful environmental conditions necessitate selecting crops for superior physiological traits with untapped potential to enhance crop performance. Plant productivity is often limited by carbon fixation rates that could be improved by increasing maximum photosynthetic carboxylation capacity ( V cmax ). However, V cmax measurements using gas exchange and biochemical assays are slow and laborious, prohibiting selection in breeding programs. Rapid hyperspectral reflectance measurements show potential for predicting V cmax using regression models. While several hyperspectral models have been developed, contributions from different spectral regions to predictions of V cmax have not been clearly identified or linked to biochemical variation contributing to V cmax . In this study, hyperspectral reflectance data from 350–2500 nm were used to build partial least squares regression models predicting in vivo and in vitro V cmax . Wild-type and transgenic tobacco plants with antisense reductions in Rubisco content were used to alter V cmax independent from chlorophyll, carbon, and nitrogen content. Different spectral regions were used to independently build partial least squares regression models and identify key regions linked to V cmax and other leaf traits. The greatest V cmax prediction accuracy used a portion of the shortwave infrared region from 2070 nm to 2470 nm, where the inclusion of fewer spectral regions resulted in more accurate models.