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ARS Home » Midwest Area » Urbana, Illinois » Global Change and Photosynthesis Research » Research » Publications at this Location » Publication #374237
Research Project: Optimizing Photosynthesis for Global Change and Improved Yield

Location: Global Change and Photosynthesis Research

Title: Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy

Author
item WANG, SHENG - University Of Illinois
item GUAN, KAIYU - University Of Illinois
item WANG, ZHIHUI - University Of Illinois
item Ainsworth, Elizabeth - Lisa
item ZHENG, TING - University Of Wisconsin
item TOWNSEND, PHILIP - University Of Wisconsin
item LI, KAIYUAN - University Of Illinois
item MOLLER, CHRISTOPHER - Oak Ridge Institute For Science And Education (ORISE)
item WU, GENGHONG - University Of Illinois
item JIANG, CHONGYA - University Of Illinois

Submitted to: Journal of Experimental Botany
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/12/2020
Publication Date: 2/2/2021
Publication URL: https://handle.nal.usda.gov/10113/7368101
Citation: Wang, S., Guan, K., Wang, Z., Ainsworth, E.A., Zheng, T., Townsend, P., Li, K., Moller, C., Wu, G., Jiang, C. 2021. Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy. Journal of Experimental Botany. 72(2):341-354. https://doi.org/10.1093/jxb/eraa432.
DOI: https://doi.org/10.1093/jxb/eraa432

Interpretive Summary: This study tested different approaches for predicting photosynthetic capacity of maize from leaf reflectance spectra. Understanding photosynthetic capacity of maize crops across wide geographic scales enables more accurate prediction of yield, crop water use and carbon cycling. This study used physical models (radiative transfer models) and statistical models to predict photosynthetic capacity as well as leaf nitrogen content and leaf chlorophyll content. We found that both approaches can accurately estimate photosynthesis, chlorophyll and leaf nitrogen content, and that photosynthetic capacity is most accurately modeled when both nitrogen and chlorophyll are used. Leveraging the advances in imaging spectroscopy, we can extend the measurements from the leaf scale to the regional scale for high-throughput and large-scale agricultural monitoring.

Technical Abstract: The photosynthetic capacity or maximum carboxylation rate of Rubisco (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine crop photosynthesis and yield. Accurate, timely, rapid and nondestructive estimates of leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring. We developed and evaluated physically-based radiative transfer models (RTMs) and statistical-based partial-least-squares regression (PLSR) models to estimate leaf photosynthetic traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) plots with diverse genotypes, growth stages, treatments of nitrogen fertilizers and ozone stresses during three growing seasons. In calibration-free approaches, the RTM method accurately estimated chlorophyll content (R2 = 0.89), while generalized PLSR was able to retrieve leaf nitrogen concentration (R2 = 0.72). Using PLSR, Vmax can be well predicted based on spectra (R2 = 0.66). The integration of chlorophyll content and nitrogen concentration estimated from leaf spectra can provide better prediction of Vmax (R2 = 0.51) than using only chlorophyll or nitrogen. This study provides new insights into Vmax prediction by sensing chlorophyll and nitrogen, and highlights the importance of understanding leaf nitrogen allocation, e.g. the information of leaf total nitrogen and chlorophyll nitrogen, for Vmax prediction.