scholarly journals Photosynthetic induction upon transfer from low to high light is affected by leaf nitrogen content in tomato

2021 ◽  
Author(s):  
Hu Sun ◽  
Yu-Qi Zhang ◽  
Shi-Bao Zhang ◽  
Wei Huang
Keyword(s):  
Electron Flow ◽  
Co2 Assimilation ◽  
High Light ◽  
Carbon Gain ◽  
Leaf Nitrogen Content ◽  
Photosynthetic Induction ◽  
N Content ◽  
Fluctuating Light ◽  
Leaf N Content ◽  
Leaf N

The response of photosynthetic CO2 assimilation to changes of illumination affects plant growth and crop productivity under natural fluctuating light conditions. However, the effects of nitrogen (N) supply on photosynthetic physiology after transition from low to high light are seldom studied. To elucidate this, we measured gas exchange and chlorophyll fluorescence under fluctuating light in tomato (Solanum lycopersicum) seedlings grown with different N conditions. After transition from low to high light, the induction speeds of net CO2 assimilation (AN), stomatal conductance (gs) and mesophyll conductance (gm) delayed with the decline in leaf N content. The times to reach 90% of maximum AN, gs and gm were negatively correlated to leaf N content. This delayed photosynthetic induction in plants grown under low N concentration was mainly caused by the slow induction response of gm rather than that of gs. Furthermore, the photosynthetic induction upon transfer from low to high light was hardly limited by photosynthetic electron flow. These results indicate that decreased leaf N content declines carbon gain under fluctuating light in tomato. Increasing the induction kinetics of gm has the potential to enhance the carbon gain of field crops grown in infertile soil.

scholarly journals Drought stress delays photosynthetic induction and accelerates photoinhibition of photosystem I under fluctuating light

2021 ◽  
Author(s):  
Hu Sun ◽  
Qi Shi ◽  
Ning-Yu Liu ◽  
Shi-Bao Zhang ◽  
Wei Huang
Keyword(s):  
Drought Stress ◽  
Gas Exchange ◽  
Photosystem I ◽  
Co2 Fixation ◽  
Electron Flow ◽  
Photosynthetic Performance ◽  
High Light ◽  
Carbon Gain ◽  
Photosynthetic Induction ◽  
Fluctuating Light

Fluctuating light (FL) and drought stress usually occur concomitantly. However, whether drought stress affects photosynthetic performance under FL remains unknown. Here, we measured gas exchange, chlorophyll fluorescence, and P700 redox state under FL in drought-stressed tomato (Solanum lycopersicum) seedlings. Drought stress significantly affected stomatal opening and mesophyll conductance after transition from low to high light and thus delayed photosynthetic induction under FL. Therefore, drought stress exacerbated the loss of carbon gain under FL. Furthermore, restriction of CO2 fixation under drought stress aggravated the over-reduction of photosystem I (PSI) upon transition from low to high light. The resulting stronger FL-induced PSI photoinhibition significantly supressed linear electron flow and PSI photoprotection. These results indicated that drought stress not only affected gas exchange under FL but also accelerated FL-induced photoinhibition of PSI. Furthermore, drought stress enhanced relative cyclic electron flow in FL, which partially compensated for restricted CO2 fixation and thus favored PSI photoprotection under FL. Therefore, drought stress has large effects on photosynthetic dark and light reactions under FL.

scholarly journals Drought stress delays photosynthetic induction and accelerates photoinhibition of photosystem I under fluctuating light

2021 ◽  
Author(s):  
Hu Sun ◽  
Qi Shi ◽  
Ning-Yu Liu ◽  
Shi-Bao Zhang ◽  
Wei Huang
Keyword(s):  
Drought Stress ◽  
Gas Exchange ◽  
Photosystem I ◽  
Co2 Fixation ◽  
Electron Flow ◽  
Photosynthetic Performance ◽  
High Light ◽  
Carbon Gain ◽  
Photosynthetic Induction ◽  
Fluctuating Light

Fluctuating light (FL) and drought stress usually occur concomitantly. However, whether drought stress affects photosynthetic performance under FL remains unknown. Here, we measured gas exchange, chlorophyll fluorescence, and P700 redox state under FL in drought-stressed tomato (Solanum lycopersicum) seedlings. Drought stress significantly affected stomatal opening and mesophyll conductance after transition from low to high light and thus delayed photosynthetic induction under FL. Therefore, drought stress exacerbated the loss of carbon gain under FL. Furthermore, restriction of CO2 fixation under drought stress aggravated the over-reduction of photosystem I (PSI) upon transition from low to high light. The resulting stronger FL-induced PSI photoinhibition significantly supressed linear electron flow and PSI photoprotection. These results indicated that drought stress not only affected gas exchange under FL but also accelerated FL-induced photoinhibition of PSI. Furthermore, drought stress enhanced relative cyclic electron flow in FL, which partially compensated for restricted CO2 fixation and thus favored PSI photoprotection under FL. Therefore, drought stress has large effects on photosynthetic dark and light reactions under FL.

scholarly journals 159 The Relationship between Actual Photosystem II Efficiency and Quantum Yield for CO2 Assimilation is Not Affected by Nitrogen Content in Apple Leaves

HortScience ◽  
2000 ◽  
Vol 35 (3) ◽  
pp. 417C-417
Author(s):  
Lailiang Cheng ◽  
Leslie H. Fuchigami ◽  
Patrick J. Breen
Keyword(s):  
Quantum Yield ◽  
Photosystem Ii ◽  
Electron Flow ◽  
Linear Electron ◽  
N Content ◽  
Psii Efficiency ◽  
Apple Leaves ◽  
Leaf N Content ◽  
The Relationship ◽  
Leaf N

Bench-grafted Fuji/M26 apple (Malus domestica Borkh) trees were fertigated with different concentrations of nitrogen by using a modified Hoagland's solution for 45 days. CO2 assimilation and actual photosystem II (PSII) efficiency in response to incident photon flux density (PFD) were measured simultaneously in recent fully expanded leaves under low O2 (2%) and saturated CO2 (1300 ppm) conditions. A single curvilinear relationship was found between true quantum yield for CO2 assimilation and actual PSII efficiency for leaves with a wide range of leaf N content. The relationship was linear up to a quantum yield of approximately 0.05 mol CO2/mol quanta, then became curvilinear with a further rise in quantum yield in response to decreasing PFD. This relationship was subsequently used as a calibration curve to assess the rate of linear electron transport associated with rubisco and partitioning of electron flow between CO2 assimilation and photorespiration in different N leaves in response to intercellular CO2 concentration (Ci) under normal O2 conditions. Both the rate of linear electron flow, and the rate to CO2 or O2 increased with increasing leaf N at any given Ci, but the percentage of linear electron flow to CO2 assimilation remained the same regardless of leaf N content. As Ci increased, the percentage of linear electron flow to CO2 assimilation increased. In conclusion, the relationship between actual PSII efficiency and quantum yield for CO2 assimilation and the partitioning of electron flow between CO2 assimilation and photorespiration are not affected by N content in apple leaves.

scholarly journals The Relationship between Leaf Nitrogen Content and Photosynthesis in Apple Leaves

HortScience ◽  
1996 ◽  
Vol 31 (4) ◽  
pp. 578c-578
Author(s):  
Lailiang Cheng ◽  
Sunghee Guak ◽  
Leslie H. Fuchigami
Keyword(s):  
Nitrogen Content ◽  
Net Photosynthesis ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Content ◽  
Nitrogen And Phosphorus ◽  
N Content ◽  
Wide Range ◽  
Leaf N Content ◽  
The Relationship ◽  
Leaf N

Fertigation of young Fuji/M26 apple trees (Malus domestica Borkh.) with different nitrogen concentrations by using a modified Hoagland solution for 6 weeks resulted in a wide range of leaf nitrogen content in recently expanded leaves (from 0.9 to 4.4 g·m–2). Net photosynthesis at ambient CO2, carboxylation efficiency, and CO2-saturated photosynthesis of recently expanded leaves were closely related to leaf N content expressed on both leaf area and dry weight basis. They all increased almost linearly with increase in leaf N content when leaf N < 2.4 g·m–2, leveled off when leaf N increased further. The relationship between stomatal conductance and leaf N content was similar to that of net photosynthesis with leaf N content, but leaf intercellular CO2 concentration tended to decrease with increase in leaf N content, indicating non-stomatal limitation in leaves with low N content. Photosynthetic nitrogen use efficiency was high when leaf N < 2.4 g·m–2, but decreased with further increase in leaf N content. Due to the correlation between leaf nitrogen and phosphorus content, photosynthesis was also associated with leaf P content, but to a lesser extent.

scholarly journals A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Qi ◽  
Yanan Zhao ◽  
Yufang Huang ◽  
Yang Wang ◽  
Wei Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Phones ◽  
Mean Value ◽  
Wheat Cultivars ◽  
N Content ◽  
Color Parameters ◽  
The Neural Network ◽  
The Mean ◽  
Leaf N Content ◽  
Leaf N

AbstractThe accurate and nondestructive assessment of leaf nitrogen (N) is very important for N management in winter wheat fields. Mobile phones are now being used as an additional N diagnostic tool. To overcome the drawbacks of traditional digital camera diagnostic methods, a histogram-based method was proposed and compared with the traditional methods. Here, the field N level of six different wheat cultivars was assessed to obtain canopy images, leaf N content, and yield. The stability and accuracy of the index histogram and index mean value of the canopy images in different wheat cultivars were compared based on their correlation with leaf N and yield, following which the best diagnosis and prediction model was selected using the neural network model. The results showed that N application significantly affected the leaf N content and yield of wheat, as well as the hue of the canopy images and plant coverage. Compared with the mean value of the canopy image color parameters, the histogram could reflect both the crop coverage and the overall color information. The histogram thus had a high linear correlation with leaf N content and yield and a relatively stable correlation across different growth stages. Peak b of the histogram changed with the increase in leaf N content during the reviving stage of wheat. The histogram of the canopy image color parameters had a good correlation with leaf N content and yield. Through the neural network training and estimation model, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the estimated and measured values of leaf N content and yield were smaller for the index histogram (0.465, 9.65%, and 465.12, 5.5% respectively) than the index mean value of the canopy images (0.526, 12.53% and 593.52, 7.83% respectively), suggesting a good fit for the index histogram image color and robustness in estimating N content and yield. Hence, the use of the histogram model with a smartphone has great potential application in N diagnosis and prediction for wheat and other cereal crops.

scholarly journals Effects of ontogenetic stage and leaf age on leaf functional traits and the relationships between traits in Pinus koraiensis

2021 ◽  
Author(s):  
Meng Ji ◽  
Guangze Jin ◽  
Zhili Liu
Keyword(s):  
Significant Positive Correlation ◽  
Leaf Age ◽  
Leaf Traits ◽  
Ontogenetic Stage ◽  
Pinus Koraiensis ◽  
Mature Trees ◽  
N Content ◽  
Ontogenetic Stages ◽  
Leaf N Content ◽  
Leaf N

AbstractInvestigating the effects of ontogenetic stage and leaf age on leaf traits is important for understanding the utilization and distribution of resources in the process of plant growth. However, few studies have been conducted to show how traits and trait-trait relationships change across a range of ontogenetic stage and leaf age for evergreen coniferous species. We divided 67 Pinus koraiensis Sieb. et Zucc. of various sizes (0.3–100 cm diameter at breast height, DBH) into four ontogenetic stages, i.e., young trees, middle-aged trees, mature trees and over-mature trees, and measured the leaf mass per area (LMA), leaf dry matter content (LDMC), and mass-based leaf nitrogen content (N) and phosphorus content (P) of each leaf age group for each sampled tree. One-way analysis of variance (ANOVA) was used to describe the variation in leaf traits by ontogenetic stage and leaf age. The standardized major axis method was used to explore the effects of ontogenetic stage and leaf age on trait-trait relationships. We found that LMA and LDMC increased significantly and N and P decreased significantly with increases in the ontogenetic stage and leaf age. Most trait-trait relationships were consistent with the leaf economic spectrum (LES) at a global scale. Among them, leaf N content and LDMC showed a significant negative correlation, leaf N and P contents showed a significant positive correlation, and the absolute value of the slopes of the trait-trait relationships showed a gradually increasing trend with an increasing ontogenetic stage. LMA and LDMC showed a significant positive correlation, and the slopes of the trait-trait relationships showed a gradually decreasing trend with leaf age. Additionally, there were no significant relationships between leaf N content and LMA in most groups, which is contrary to the expectation of the LES. Overall, in the early ontogenetic stages and leaf ages, the leaf traits tend to be related to a "low investment-quick returns" resource strategy. In contrast, in the late ontogenetic stages and leaf ages, they tend to be related to a "high investment-slow returns" resource strategy. Our results reflect the optimal allocation of resources in Pinus koraiensis according to its functional needs during tree and leaf ontogeny.

scholarly journals Variation of nitric oxide emission potential in plants: a possible link to leaf N content and net photosynthetic activity

2014 ◽  
Vol 8 (3) ◽  
pp. 313-320 ◽  
Author(s):  
Juan Chen ◽  
Chao Wang ◽  
Fei-Hua Wu ◽  
Wen-Hua Wang ◽  
Ting-Wu Liu ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Photosynthetic Activity ◽  
N Content ◽  
Nitric Oxide Emission ◽  
Leaf N Content ◽  
Leaf N

scholarly journals High-yielding rice Takanari has superior photosynthetic response to a commercial rice Koshihikari under fluctuating light

2019 ◽  
Vol 70 (19) ◽  
pp. 5287-5297 ◽  
Author(s):  
Shunsuke Adachi ◽  
Yu Tanaka ◽  
Atsuko Miyagi ◽  
Makoto Kashima ◽  
Ayumi Tezuka ◽  
...  
Keyword(s):  
Electron Transport ◽  
Stomatal Conductance ◽  
Metabolic Flux ◽  
Electron Transport Rate ◽  
Rice Cultivar ◽  
Carbon Gain ◽  
High Electron ◽  
Photosynthetic Induction ◽  
Photosynthetic Response ◽  
Fluctuating Light

The high-yielding rice cultivar Takanari has fast photosynthetic induction owing to a high electron transport rate, stomatal conductance, and metabolic flux, leading to high daily carbon gain under fluctuating light.

scholarly journals Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hiroto Yamashita ◽  
Rei Sonobe ◽  
Yuhei Hirono ◽  
Akio Morita ◽  
Takashi Ikka
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Visible Region ◽  
Machine Learning Algorithms ◽  
Hyperspectral Reflectance ◽  
N Content ◽  
Green Leaves ◽  
Leaf N Content ◽  
N Status ◽  
Leaf N

Abstract Nondestructive techniques for estimating nitrogen (N) status are essential tools for optimizing N fertilization input and reducing the environmental impact of agricultural N management, especially in green tea cultivation, which is notably problematic. Previously, hyperspectral indices for chlorophyll (Chl) estimation, namely a green peak and red edge in the visible region, have been identified and used for N estimation because leaf N content closely related to Chl content in green leaves. Herein, datasets of N and Chl contents, and visible and near-infrared hyperspectral reflectance, derived from green leaves under various N nutrient conditions and albino yellow leaves were obtained. A regression model was then constructed using several machine learning algorithms and preprocessing techniques. Machine learning algorithms achieved high-performance models for N and Chl content, ensuring an accuracy threshold of 1.4 or 2.0 based on the ratio of performance to deviation values. Data-based sensitivity analysis through integration of the green and yellow leaves datasets identified clear differences in reflectance to estimate N and Chl contents, especially at 1325–1575 nm, suggesting an N content-specific region. These findings will enable the nondestructive estimation of leaf N content in tea plants and contribute advanced indices for nondestructive tracking of N status in crops.

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