High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity

Abstract

Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurements of key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods often rely on statistical approaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the mechanistic uncertainty of spectroscopic techniques, genetic modification of plant biochemical pathways may affect reflectance spectra causing predictive models to lose power. The objectives of this research were to assess over two separate years, whether a predictive model can represent natural and imposed variation in leaf photosynthetic potential for different crop cultivars and genetically modified plants, to assess the interannual capabilities of a partial least square regression (PLSR) model, and to determine whether leaf N is a dominant driver of photosynthesis in PLSR models. In 2016, a PLSR analysis of reflectance spectra coupled with gas exchange data was used to build predictive models for photosynthetic parameters including maximum carboxylation rate of Rubisco (Vc,max), maximum electron transport rate (Jmax) and percentage leaf nitrogen ([N]). The model was developed for wild type and genetically modified plants that represent a wide range of photosynthetic capacities. Results show that hyperspectral reflectance accurately predicted Vc,max, Jmax and [N] for all plants measured in 2016. Applying these PLSR models to plants grown in 2017 resulted in a strong predictive ability relative to gas exchange measurements for Vc,max, but not for Jmax, and not for genotypes unique to 2017. Building a new model including data collected in 2017 resulted in more robust predictions, with R2 increases of 17% for Vc,max. and 13% Jmax. Plants generally have a positive correlation between leaf nitrogen and photosynthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher [N] despite much lower Vc,max. The PLSR model was able to accurately predict both lower Vc,max and higher leaf [N] for this genotype suggesting that the spectral based estimates of Vc,max and leaf nitrogen [N] are independent. These results suggest that the PLSR model can be applied across years, but only to genotypes used to build the model and that the actual mechanism measured with the PLSR technique is not directly related to leaf [N]. The success of the leaf-scale analysis suggests that similar approaches may be successful at the canopy and ecosystem scales but to use these methods across years and between genotypes at any scale, application of accurately populated physical based models based on radiative transfer principles may be required.

Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression

“…This study provided direct evidence that mapping V cmax and J max at the canopy level could be successful with reflectance spectra (or derived variables) from 400 to 900 nm used as predictors. The findings shown in Figure were in agreement with previous studies to show the PLSR model as an effective tool to predict photosynthetic variables across different spatial scales (Ainsworth et al, ; Dechant et al, ; Fu et al, ; Meacham‐Hensold et al, ; Serbin et al, ; Serbin et al, ). However, this study only used reflectance spectra from 400 to 900 nm for the PLSR modelling, which exhibited an even higher or at least similar R 2 value compared with previous modelling results at the leaf level (e.g., Dechant et al, ).…”

“…A higher nitrogen content per area was generally translated to a greater photosynthetic capacity due to its importance as a component of the enzyme Rubisco (Wright et al, ). However, the correlation/regression coefficient in this study did not suggest a dominating factor of nitrogen content to explain photosynthetic variations among crop cultivars, which would probably be explained by the fact that species used in this study included both wild‐type and genetically modified cultivars that may decouple the well‐known relationship between leaf nitrogen and photosynthetic capacities (Meacham‐Hensold et al ).…”

“…Results presented in this study showed that V cmax and J max could be well estimated for 11 cultivars of one crop (including both genetically modified and wild types) using the proposed three approaches (i.e., PLSR of reflectance spectra, spectral indices, and RTM‐inversion of crop traits) with R 2 all larger than 0.6. These modelling performances at the canopy level using reflectance spectra from 400 to 900 nm were even better than those at the leaf level using a similar dataset from the whole spectrum (400–2,500 nm; Fu et al, ; Meacham‐Hensold et al, ). A similar finding was also observed in comparisons between Serbin et al () and Serbin et al () for tree species: V cmax were better predicted at the canopy level ( R 2 = 0.94) than at the leaf level ( R 2 = 0.89).…”

“…These genetically modified tobacco cultivars may show a V cmax ( J max ) value larger than 300 μmol m −2 s −1 , for example, due to increased carbon reduction enzymes (increase in the electron transport metabolite pools). Further details of these 11 cultivars can be found in Meacham‐Hensold et al ().…”

Natural genetic variation in photosynthesis: an untapped resource to increase crop yield potential?

“…Furthermore, new tools are needed to facilitate high‐throughput measurements of photosynthetic capacity in situ and on large numbers of plants, such as the recent developments in hyperspectral imaging to rapidly measure V cmax in the field (ca. 10 sec) (Meacham‐Hensold et al , ). Although the approaches mentioned above have made significant advancements in measuring photosynthetic capacity, further developments on instrumentation are necessary to enable diel operational or realized photosynthetic rates to be determined, that are subject to the limitations driven by the growth conditions as well as the kinetics of various processes that a plant is subjected to over the dynamic diurnal period.…”