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

• Published in: Plant, cell and environment cell and environment, 2022-01, Vol.45 (1), p.80-94
• Main Authors: Kumagai, Etsushi, Burroughs, Charles H., Pederson, Taylor L., Montes, Christopher M., Peng, Bin, Kimm, Hyungsuk, Guan, Kaiyu, Ainsworth, Elizabeth A., Bernacchi, Carl J.
• Format: Article
• Language: English
• Subjects: Acclimation; Acclimatization; Biochemistry; Canopies; Carboxylation; climate change; Crop production; Electron transport; Gas exchange; Glycine max; Glycine max - physiology; Growing season; High temperature; high‐throughput phenotyping; Illinois; leaf nitrogen content; Learning algorithms; Leaves; Machine Learning; machine learning regression; Mathematical models; Models, Biological; Nitrogen - analysis; Parameters; Photosynthesis; Photosynthesis - physiology; Plant Leaves - chemistry; Plant Leaves - physiology; Prediction models; Reflectance; Soybeans; Spectral reflectance; Temperature; Temporal resolution
• ISSN: 0140-7791; 1365-3040
• DOI: 10.1111/pce.14204

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 provides reliable measures of Vcmax25 and Jmax25; however, the capability of this method to capture biochemical acclimations of the two parameters to high growth temperature over time has not been demonstrated. In this study, Vcmax25 and Jmax25 were measured over multiple growth stages during two growing seasons for field‐grown soybeans using both gas exchange techniques and leaf spectral reflectance under ambient and four elevated canopy temperature treatments (ambient+1.5, +3, +4.5, and +6°C). 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 Vcmax25 and Jmax25, respectively. Hyperspectral reflectance captured biochemical acclimation of leaf photosynthesis to high temperature in the field, improving spatial and temporal resolution in the ability to assess the impact of future warming on crop productivity. Seasonally dependent acclimation of leaf photosynthetic biochemistry was found in field‐grown soybean under full season warming. Hyperspectral reflectance measurements coupled with machine learning regressions can be used to predict photosynthetic biochemical acclimation to high temperature.