Adult Eucalyptus pauciflora leaves are vertically displayed. They have multiple palisade cell layers beneath both surfaces, interrupted by numerous oil glands. Here, we characterized light absorption, chlorophyll, photosynthetic capacity and CO2 fixation profiles through these leaves. Multiple chlorophyll fluorescence images of leaves viewed in cross-section were made by applying light from different directions. 14CO2 labelling, followed by paradermal cryosectioning, was used to measure profiles of photosynthesis. Photosynthetic capacity peaked 75 microm into the mesophyll beneath each surface and was lowest in the centre of the 600-microm-thick leaf. Predictions by a multilayer model using Beer's law matched the observed profiles of 14C fixation. When constrained to the horizontal, a vertically acclimated leaf gains only 79% of the daily photosynthesis achieved by a horizontally acclimated leaf. However, it outperforms the horizontally acclimated leaf when both are oriented vertically. Each half of the observed profile of photosynthetic capacity closely matches the profile of light absorption through the leaf with unilateral illumination to that surface. Derivation of biochemical parameters from gas exchange measured under unilateral illumination would underestimate the real photosynthetic capacity of these leaves by 21%.
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http://dx.doi.org/10.1111/j.1469-8137.2006.01789.x | DOI Listing |
Environ Monit Assess
January 2025
Amity Institute of Biotechnology, Amity University, Noida, Uttar Pradesh, India.
In recent years, heightened concern has emerged regarding the pervasive presence of microplastics in the environment, particularly in aquatic ecosystems. This concern has prompted extensive scientific inquiry into microplastics' ecological and physiological implications, including threats to biodiversity. The robust adsorption capacity of microplastic surfaces facilitates their widespread distribution throughout aquatic ecosystems, acting also as carriers of organic pollutants.
View Article and Find Full Text PDFFront Plant Sci
January 2025
College of Agriculture, Shanxi Agricultural University, Taigu, Shanxi, China.
Chlorophyll density (ChD) can reflect the photosynthetic capacity of the winter wheat population, therefore achieving real-time non-destructive monitoring of ChD in winter wheat is of great significance for evaluating the growth status of winter wheat. Derivative preprocessing has a wide range of applications in the hyperspectral monitoring of winter wheat chlorophyll. In order to research the role of fractional-order derivative (FOD) in the hyperspectral monitoring model of ChD, this study based on an irrigation experiment of winter wheat to obtain ChD and canopy hyperspectral reflectance.
View Article and Find Full Text PDFBackground: The photothermal sensitivity of tobacco refers to how tobacco plants respond to variations in the photothermal conditions of their growth environment. The degree of this sensitivity is crucial for determining the optimal planting regions for specific varieties, as well as for improving the quality and yield of tobacco leaves. However, the precise mechanisms underlying the development of photothermal sensitivity in tobacco remain unclear.
View Article and Find Full Text PDFPlant Physiol Biochem
January 2025
Agricultural College, Anhui Agricultural University, 230036, Hefei, China; Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry (CIC-MCP), 210095, Nanjing, China. Electronic address:
Nitric oxide (NO) positively contributes to maintaining a high photosynthetic rate in waterlogged-wheat plants by maintaining high stomatal conductance (g), mesophyll conductance (g), and electron transport rates in PSII (J). However, the molecular mechanisms underlying the synergistic regulation of photosynthetic characteristics during wheat waterlogging remain unclear. Pot experiments were conducted with two cultivars: Yangmai15 (YM15: high waterlogging-tolerance capacity) and Yangmai24 (YM24: conventional waterlogging-tolerance capacity).
View Article and Find Full Text PDFSpectral analysis is a widely used method for monitoring photosynthetic capacity. However, vegetation indices-based linear regression exhibits insufficient utilization of spectral information, while full spectra-based traditional machine learning has limited representational capacity (partial least squares regression) or uninterpretable (convolution). In this study, we proposed a deep learning model with enhanced interpretability based on attention and vegetation indices calculation for global spectral feature mining to accurately estimate photosynthetic capacity.
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