Source:sink imbalance detected with leaf‐ and canopy‐level spectroscopy in a field‐grown crop

Abstract

The finely tuned balance between sources and sinks determines plant resource partitioning and regulates growth and development. Understanding and measuring metabolic indicators of source or sink limitation forms a vital part of global efforts to increase crop yield for future food security. We measured metabolic profiles of Cucurbita pepo (zucchini) grown in the field under carbon sink limitation and control conditions. We demonstrate that these profiles can be measured non-destructively using hyperspectral reflectance at both leaf and canopy scales. Total non-structural carbohydrates (TNC) increased 82% in sink-limited plants; leaf mass per unit area (LMA) increased 38% and free amino acids increased 22%. Partial least-squares regression (PLSR) models link these measured functional traits with reflectance data, enabling high-throughput estimation of traits comprising the sink limitation response.Leaf-and canopy-scale models for TNC had R 2 values of 0.93 and 0.64 and %RMSE of 13 and 38%, respectively. For LMA, R 2 values were 0.91 and 0.60 and %RMSE 7 and 14%; for free amino acids, R 2 was 0.53 and 0.21 with %RMSE 20 and 26%.Remote sensing can enable accurate, rapid detection of sink limitation in the field at the leaf and canopy scale, greatly expanding our ability to understand and measure metabolic responses to stress.

Can we improve the chilling tolerance of maize photosynthesis through breeding?

“…In particular, reflectance spectroscopy offers another high-throughput approach. A major advantage of this technique is that similar to fluorescence techniques, a rapid measurement (~1 s) enables the simultaneous estimation of a suite of metabolic and physiological parameters of interest via correlative models ( Yendrek et al , 2017 ; Ely et al , 2019 ; Burnett et al ., 2021 a , b , c ). For example, following the development of training datasets and models which are appropriate for the genotypes and traits of interest, the maximum carboxylation rate of Rubisco ( Serbin et al , 2012 ; Meacham-Hensold et al , 2020 ), leaf protein and sugar content ( Ely et al , 2019 ), ABA ( Burnett et al , 2021 b ), and chlorophyll content ( Yendrek et al , 2017 ) may all be predicted from a single hyperspectral measurement.…”

Estimating Photosynthetic Attributes from High-Throughput Canopy Hyperspectral Sensing in Sorghum

“…Additionally, lower RMSE at canopy level than leaf level has been reported for LMA estimations, as multiple scattering in the upper canopy leaf layers could strengthen the expression of key leaf properties in a closed canopy compared with leaf-level measurements [ 83 ]. A more recent study in the C 3 crop zucchini using both leaf- and canopy-level hyperspectral reflectance and PLSR has successfully predicted LMA with R 2 of 0.91 and 0.60, respectively [ 92 ]. In the present study, low RMSE (6% of mean LMA) and medium to high R 2 of 0.68 were found in the LMA estimations from canopy hyperspectral reflectance using PLSR.…”

“…Additionally, the photosynthetic capacity is colimited by Rubisco activity ( V cmax ) and RuBP regeneration, which depends on electron transport ( J max ) and the coordination of Calvin cycle enzymes [ 11 , 93 ]. Enzyme interactions in the Calvin cycle are highly complex [ 92 ], and further studies are needed to explore the relevance of the V cmax QTL detected here.…”

Prediction of Photosynthetic, Biophysical, and Biochemical Traits in Wheat Canopies to Reduce the Phenotyping Bottleneck

“…Spectral data collected in the field has been used to calculate spectral indices or the full reflectance signature of an area of the electromagnetic spectrum, usually ranging from 350 to 2,500 nm to predict physiological traits at leaf or canopy scales ( Ollinger, 2011 ; Gamon et al, 2019 ; Robles-Zazueta et al, 2021 ). Among the methods using the full spectral range, partial least squares regression (PLSR) modeling has become the gold standard for HTP modeling of physiological traits, such as leaf A sat ; V cmax ; J max ; dark respiration; leaf osmotic potential; leaf C, N, and chlorophyll content; protein; phenols; sugars; leaf mass area; and specific leaf area ( Serbin et al, 2012 ; Silva-Perez et al, 2018 ; Coast et al, 2019 ; Cotrozzi and Couture, 2020 ; Burnett et al, 2021 ; Furbank et al, 2021 ). Even though using hyperspectral reflectance data to predict physiological traits is not novel, the use of the spectra to predict photosynthetic, biophysical, and biochemical traits along different leaf layers has not been explored so far.…”