TY - JOUR
T1 - High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity
AU - Meacham-Hensold, Katherine
AU - Montes, Christopher M.
AU - Wu, Jin
AU - Guan, Kaiyu
AU - Fu, Peng
AU - Ainsworth, Elizabeth A.
AU - Pederson, Taylor
AU - Moore, Caitlin E.
AU - Brown, Kenny Lee
AU - Raines, Christine
AU - Bernacchi, Carl J.
N1 - Funding Information:
The information, data, or work presented herein was funded in parts by (1) Bill and Melinda Gates Foundation grant OPP1060461 , titled “RIPE—Realizing increased photosynthetic efficiency for sustainable increases in crop yield”, (2) by the Advanced Research Projects Agency - Energy - U.S. Department of Energy , under Award Number DE-AR0000598 , and (3) by the Agricultural Research Service of the United States Department of Agriculture. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. We thank David Drag and Ben Harbaugh for greenhouse and field plant management. Prof. Susanne von Caemmerer (ARC Centre of Excellence for Translational Photosynthesis Research, Australian National University) kindly provided the Rubisco Antisense Nicotiana tabacum and discussion that helped improve this work. Johannes Kromdijk, Katarzyna Glowaka and Stephen P. Long provided transgenic N. tabacum lines 43-OE and 4-KO and Paul South and Donald R. Ort provided line 200-8. Co-author J.W. was supported by the United States Department of Energy contract No. DE-SC0012704 to Brookhaven National Laboratory. We also thank Evan Dracup, Justine Brumm, Kyle Coffland, Morgan Prinn, Alyssa Dwyer, Alex Riley, Isaac Howenstein, Jennifer Ward, Amanda Cavanagh, and Elena Pelech for assistance with the field work.
Funding Information:
The information, data, or work presented herein was funded in parts by (1) Bill and Melinda Gates Foundation grant OPP1060461, titled “RIPE—Realizing increased photosynthetic efficiency for sustainable increases in crop yield”, (2) by the Advanced Research Projects Agency - Energy - U.S. Department of Energy, under Award Number DE-AR0000598, and (3) by the Agricultural Research Service of the United States Department of Agriculture. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. We thank David Drag and Ben Harbaugh for greenhouse and field plant management. Prof. Susanne von Caemmerer (ARC Centre of Excellence for Translational Photosynthesis Research, Australian National University) kindly provided the Rubisco Antisense Nicotiana tabacum and discussion that helped improve this work. Johannes Kromdijk, Katarzyna Glowaka and Stephen P. Long provided transgenic N. tabacum lines 43-OE and 4-KO and Paul South and Donald R. Ort provided line 200-8. Co-author J.W. was supported by the United States Department of Energy contract No. DE-SC0012704 to Brookhaven National Laboratory. We also thank Evan Dracup, Justine Brumm, Kyle Coffland, Morgan Prinn, Alyssa Dwyer, Alex Riley, Isaac Howenstein, Jennifer Ward, Amanda Cavanagh, and Elena Pelech for assistance with the field work. Katherine Meacham-Hensold and Carl Bernacchi designed the experiment. Katherine Meacham-Hensold carried out field work and data analysis and led the development of the manuscript. Christopher M. Montes carried out data analysis and assisted with building of PLSR models. Jin Wu and Kaiyu Guan helped with experimental design, technical equipment set up and advice on data analysis. Peng Fu advised on data analysis and manuscript editing. Taylor Pederson and Caitlin Moore helped with technical aspects of data collection. Elizabeth Ainsworth helped with data analysis and experimental design. Christine Raines and Kenny Lee Brown provided the construct and experimental design for SFX genotype. Carl Bernacchi supervised the work as lab leader, advising on experimental design and data analysis. All authors contributed to editing all drafts of the manuscript. There are no conflicts of interest to declare.
Publisher Copyright:
© 2019
PY - 2019/9/15
Y1 - 2019/9/15
N2 - 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.
AB - 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.
KW - Food security
KW - Gas exchange
KW - Hyperspectral reflectance
KW - Leaf nitrogen
KW - Partial least squares regression (PLSR)
KW - Photosynthesis
KW - Spectroscopy
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U2 - 10.1016/j.rse.2019.04.029
DO - 10.1016/j.rse.2019.04.029
M3 - Article
C2 - 31534277
AN - SCOPUS:85068453894
SN - 0034-4257
VL - 231
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 111176
ER -