Predicting biochemical acclimation of leaf photosynthesis in soybean under in-field canopy warming using hyperspectral reflectance

Authors: KUMAGAI, ETSUSHI - National Agriculture And Food Research Organization (NARO), Agricultrual Research Center; BURROUGHS, CHARLES - University Of Illinois; PEDERSON, TAYLOR - University Of Illinois; Montes, Christopher; PENG, BIN - University Of Illinois; KIMM, HYUNGSUK - University Of Illinois; GUAN, KAIYU - University Of Illinois; Ainsworth, Elizabeth - Lisa; Bernacchi, Carl

Submitted to: Plant Cell and Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/4/2021
Publication Date: 1/1/2022
Citation: Kumagai, E., Burroughs, C., Pederson, T., Montes, C.M., Peng, B., Kimm, H., Guan, K., Ainsworth, E.A., Bernacchi, C.J. 2022. Predicting biochemical acclimation of leaf photosynthesis in soybean under in-field canopy warming using hyperspectral reflectance. Plant Cell and Environment. 45(1):80-94. https://doi.org/10.1111/pce.14204.
DOI: https://doi.org/10.1111/pce.14204

Interpretive Summary: Photosynthesis is the process that drives plant growth by removing carbon dioxide from the atmosphere and incorporating it into plant biomass, which makes up all the organs of a plant. Directly measuring how much carbon dioxide enters a plant is the most direct means to measure photosynthesis, but this technique takes a significant amount of time and effort. Indirect techniques to measure photosynthesis have been developed and used to monitor crop growth with success, and these newer techniques require must less time and effort than gas exchange techniques. This paper uses remote sensing techniques under field conditions for soybean growing under current atmospheric conditions and treatments where the plant canopies are warmed. The results build upon existing research by showing the impact of elevated temperature on photosynthesis for soybean using high throughput techniques and allows for much better time-resolved information than can be achieved using older, slower techniques.

Technical Abstract: Traditional gas exchange measurements are cumbersome which makes it difficult to capture variation in biochemical parameters, namely the maximum rate of carboxylation measured at a reference temperature (Vcmax25) and the maximum electron transport at a reference temperature (Jmax25), in response to growth temperature over time from days to weeks. Hyperspectral reflectance is shown to provide reliable measures of the two parameters; however, the capability for this method to capture biochemical acclimations of Vcmax25 and Jmax25 to high growth temperature over time has not been demonstrated. In this study, Vcmax25 and Jmax25 were measured from gas exchange techniques and leaf spectral reflectance were measured over multiple growth stages for field-grown soybeans under ambient and four elevated canopy temperature treatments (ambient+1.5, +3, +4.5 and +6 °C) during two seasons. Spectral vegetation indices and machine learning methods were used to build predictive models for Vcmax25 and Jmax25, based on the leaf reflectance. Results showed that these models yielded an R2 of 0.57-0.65 and 0.48-0.58 for predicting of Vcmax25 and Jmax25, respectively. Hyperspectral reflectance captured rapidly and accurately biochemical acclimation of leaf photosynthesis to high temperature in the field, enhancing our ability to assess the impact of future warming on agroecosystem productivity.