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Passive measurement of sun-induced chlorophyll fluorescence (F) represents the most promising tool to quantify changes in photosynthetic functioning on a large scale. However, the complex relationship between this signal and other photosynthesis-related processes restricts its interpretation under stress conditions. To address this issue, we conducted a field campaign by combining daily airborne and ground-based measurements of F (normalized to photosynthetically active radiation), reflectance and surface temperature and related the observed changes to stress-induced variations in photosynthesis. A lawn carpet was sprayed with different doses of the herbicide Dicuran. Canopy-level measurements of gross primary productivity indicated dosage-dependent inhibition of photosynthesis by the herbicide. Dosage-dependent changes in normalized F were also detected. After spraying, we first observed a rapid increase in normalized F and in the Photochemical Reflectance Index, possibly due to the blockage of electron transport by Dicuran and the resultant impairment of xanthophyll-mediated non-photochemical quenching. This initial increase was followed by a gradual decrease in both signals, which coincided with a decline in pigment-related reflectance indices. In parallel, we also detected a canopy temperature increase after the treatment. These results demonstrate the potential of using F coupled with relevant reflectance indices to estimate stress-induced changes in canopy photosynthesis.
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http://dx.doi.org/10.1111/pce.13754 | DOI Listing |
Nat Plants
July 2025
Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA.
Projected increases in the intensity and frequency of droughts in the twenty-first century are expected to cause a substantial negative impact on terrestrial gross primary productivity (GPP). Yet, the relative role of soil water supply (indicated by soil moisture) and atmospheric water demand (indicated by vapour pressure deficit, VPD) on GPP remains debated, primarily due to their strong covariations, the presence of confounding factors and unresolved causal relationships among the interconnected hydrometeorological drivers of GPP. Here using a causality-guided explainable artificial intelligence framework, we show that soil moisture is the dominant regulator of water stress, surpassing the role of VPD, when and where soil water supply limits ecosystem functions.
View Article and Find Full Text PDFNat Plants
July 2025
Jiangsu International Joint Carbon Neutrality Laboratory, Nanjing University, Nanjing, China.
Forest biodiversity plays a critical role in sustaining ecosystem functioning and buffering the effects of increased extreme weather events on forests. A global assessment of the relationship between biodiversity and photosynthesis in natural forest ecosystems, however, remains elusive. We used a large dataset of the richness of tree species from a large number of globally distributed forest plots combined with satellite retrievals of sun-induced chlorophyll fluorescence, a novel proxy for photosynthesis, to evaluate the relationship between forest biodiversity and photosynthesis and its biological mechanisms at the global scale.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
June 2025
Space Security Center, Space Engineering University, Beijing, 101416, China.
Plant Methods
June 2025
Department of Agriculture, Forestry and Bioresources, College of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, South Korea.
Background: Rice blast, one of the major diseases causing significant rice yield loss, downregulates the photosynthetic activity and induces aggressive spread of cell death causing food security concerns. Hence, earlier quantification of rice blast is imperative for improved management of the disease. Instantaneous chlorophyll fluorescence (e.
View Article and Find Full Text PDFSci Rep
April 2025
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China.
Accurate prediction of maize yields is crucial for effective crop management. In this paper, we propose a novel deep learning framework (CNNAtBiGRU) for estimating maize yield, which is applied to typical black soil areas in Northeast China. This framework integrates a one-dimensional convolutional neural network (1D-CNN), bidirectional gated recurrent units (BiGRU), and an attention mechanism to effectively characterize and weight key segments of input data.
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