Abstract
Photosynthesis is currently measured using time-laborious and/or destructive methods which slows research and breeding efforts to identify crop germplasm with higher photosynthetic capacities. We present a plot-level screening tool for quantification of photosynthetic parameters and pigment contents that utilizes hyperspectral reflectance from sunlit leaf pixels collected from a plot (~2 m×2 m) in <1 min. Using field-grown Nicotiana tabacum with genetically altered photosynthetic pathways over two growing seasons (2017 and 2018), we built predictive models for eight photosynthetic parameters and pigment traits. Using partial least squares regression (PLSR) analysis of plot-level sunlit vegetative reflectance pixels from a single visible near infra-red (VNIR) (400-900 nm) hyperspectral camera, we predict maximum carboxylation rate of Rubisco (Vc,max, R2=0.79) maximum electron transport rate in given conditions (J1800, R2=0.59), maximal light-saturated photosynthesis (Pmax, R2=0.54), chlorophyll content (R2=0.87), the Chl a/b ratio (R2=0.63), carbon content (R2=0.47), and nitrogen content (R2=0.49). Model predictions did not improve when using two cameras spanning 400-1800 nm, suggesting a robust, widely applicable and more 'cost-effective' pipeline requiring only a single VNIR camera. The analysis pipeline and methods can be used in any cropping system with modified species-specific PLSR analysis to offer a high-throughput field phenotyping screening for germplasm with improved photosynthetic performance in field trials.
Highlights
Projected population increase and pressures on land and agricultural resource availability induced by a changing global climate is placing increased demand to secure global food supply in the coming decades (Tilman et al, 2009; Foley et al, 2011)
While photosynthetic capacity has been successfully estimated from hyperspectral imaging at the ecosystem scale (Serbin et al, 2015), it is often too coarse in spatial resolution to discriminate in mixed germplasm field trails.While hyperspectral analysis has predicted leaf-level photosynthetic capacities and pigment contents (Serbin et al, 2012; Ainsworth et al, 2014; Yendrek et al, 2017; Silva-Perez et al, 2018), it has limitations as leaf clip measurements only pinpoint a few individual leaves in a plot canopy
Results show that photosynthetic capacity (Vc,max and J1800), maximum light-saturated photosynthesis (Pmax), and associated photosynthetic pigment contents (C, N, chlorophyll, and Chl a:b) can be predicted using high-throughput proximal plot-level hyperspectral imaging
Summary
Projected population increase and pressures on land and agricultural resource availability induced by a changing global climate is placing increased demand to secure global food supply in the coming decades (Tilman et al, 2009; Foley et al, 2011). Crop scientists and breeders face the challenge of characterizing genetic improvements in field trials in a high-throughput manner as a screening tool to identify ‘photosynthetically superior’ germplasm (Furbank and Tester, 2011). While photosynthetic capacity has been successfully estimated from hyperspectral imaging at the ecosystem scale (Serbin et al, 2015), it is often too coarse in spatial resolution to discriminate in mixed germplasm field trails.While hyperspectral analysis has predicted leaf-level photosynthetic capacities and pigment contents (Serbin et al, 2012; Ainsworth et al, 2014; Yendrek et al, 2017; Silva-Perez et al, 2018), it has limitations as leaf clip measurements only pinpoint a few individual leaves in a plot canopy. There are limited tools to screen a whole plot, rather than individual leaves, for photosynthetic performance. Plot-level estimations with proximal sensing platforms are needed to allow rapid capture of reflectance from all sunlit vegetation in the sensor range, eliminating the need to make assumptions about plot performance based on leaf-level samples, and expanding the spatial and temporal capabilities of analysis to capture hundreds of plots in a single day
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