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.

PREDIKSI KANDUNGAN NITROGEN DAUN PADI DENGAN ANALISIS PERGESERAN TEPI KANAL MERAH (RED EDGE SHIFT) DATA HIPERSPEKTRAL

2013 ◽  
Vol 11 (3) ◽  
Author(s):  
Nadirah Nadirah ◽  
Bangun Muljosukojo ◽  
Teguh Hariyanto ◽  
M Sadly ◽  
M Evri ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Biochemical Parameter ◽  
Leaf Nitrogen ◽  
Growth Stages ◽  
Similar Response ◽  
Leaf Nitrogen Content ◽  
N Content ◽  
N Level ◽  
Red Edge ◽  
Leaf N

Canopy hyperspectral with various growth stages measured by using field spectroradiometer (350 - 1000 nm) corresponded to leaf Nitrogen content of three rice cultivars (Ciherang, Cilamaya and IR64) during growth season in Java Island,Indonesia. Coinciding with hyperspectral measurement, biochemical parameter such as leaf Nitrogen content (g/100 gr) was analyzed from destructive biomass sample through laboratory analysis. The potential narrow band in the red edgeregion was investigated to predict leaf nitrogen content (N content) with applying modified polynomial interpolation (MPI) and modified four points linear interpolation (MFLI) methods. First derivative reflectance derived from reflectance data andsubsequently used in analysis of Red Edge Position (REP). The correlation REPMFLI was generally stronger than REP-MPI attributed to leaf N content for several level of N application that indicated by value of R2. The response of REP-MFLItoward N level 69 kg/ha exhibited the most significant correlation (R2 = 0.754) than other correlations. Meanwhile, the response of REP-MPI toward N level 161 kg/ha denoted the most significant correlation (R2 = 0.8) than other correlations. The highest correlation using REP-MPI (R2 = 0.8) to predict leaf N contentdemonstrated slightly higher than that of REP- MFLI (R2 = 0.754). In general both REP-MFLI and REP-MPI represented somewhat similar response toward N levels, such as 103.5 kg/h, 115 kg/ha. The exploration of characteristics of red edge shiftis a fundamental point in developing rapid and precise prediction for biochemical parameter. In addition, its prediction capability was promising to support crop farming management.

scholarly journals Evaluation of the Use of SPAD-502 Chlorophyll meter for non-destructive estimation of nitrogen status of tea leaf (Camellia sinensis L. Kuntze)

2019 ◽  
Author(s):  
Wahono A. Wahono ◽  
D. Indradewa ◽  
B. H. Sunarminto ◽  
E. Haryono ◽  
D. Prajitno
Keyword(s):  
Nitrogen Content ◽  
Nitrogen Fertilizer ◽  
Nutrient Management ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Content ◽  
Nitrogen Status ◽  
Chlorophyll Meter ◽  
Leaf N Content ◽  
Tea Leaf ◽  
Spad Readings

Efficient nutrient management requires estimating factual fertilizer requirements. This study was aimed to test the use of chlorophyll meter SPAD-502 to estimate the nitrogen status of tea maintenance leaf. The test was carried out by correlating the SPAD readings with destructively measured leaf nitrogen content using samples oGbtained from nitrogen fertilizer dosage experiments. Observations were made at 15, 32, 45 and 62 days after the application of N fertilizer treatments. The results showed that the SPAD readings and total nitrogen leaf content correlated significantly with the time of observation. Estimation of leaf N content based on the SPAD readings follows linear line equation y = 0.0311x + 1.5856 with coefficient determinant (R²) = 0.62 significantly at P less than 0.01. It was concluded that SPAD-502 chlorophyll meter is reliable to assess the leaf nitrogen content of tea maintenance leaf and is adequate to predict future nitrogen fertilizer requirements.

Effect of Nitrogen Content on the Photosynthetic Characteristics of Sunflower Leaves

1993 ◽  
Vol 20 (3) ◽  
pp. 251 ◽  
Author(s):  
DJ Connor ◽  
AJ Hall ◽  
VO Sadras
Keyword(s):  
Nitrogen Content ◽  
Dark Respiration ◽  
Net Photosynthesis ◽  
Leaf Nitrogen ◽  
Mean Value ◽  
Life Cycles ◽  
High Irradiance ◽  
Response Curves ◽  
Unit Leaf ◽  
Wide Range

Photosynthesis-irradiance response curves and leaf nitrogen contents were measured weekly by destructive sampling over the life cycles of leaves 10, 15, 20 and 25 of sunflower plants (cv. Prosol 35) grown in large pots in the open under optimum conditions of temperature and high irradiance. Individual leaf responses were adequately described by a hyperbola of three parameters, viz. Pmax, the rate of photosynthesis in saturating irradiance; R, the rate of dark respiration adjusted for temperature (30�C); and ε, the apparent quantum efficiency of photosynthesis at low irradiance. Pmax (range 0-40 μmol CO2 m-2 s-1) and R (0-4 μmol CO2 m-2 s-1) were non-linearly related to nitrogen content per unit leaf area (NL) (range 0.3-2.9 g N m-2) across all leaf positions and for all leaf ages. ε (mean value 0.050 mol mol-1, s.e. 0.001) was independent of NL. The equations for net photosynthesis derived from pot studies were shown to explain (r2 =0.80) leaf photosynthesis in a crop of the same cultivar over a wide range of NL and irradiance.

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 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 504 Urea Uptake and Nitrogen Mobilization by Apple Leaves in Relation to Tree Nitrogen Status in the Fall

HortScience ◽  
2000 ◽  
Vol 35 (3) ◽  
pp. 481D-481
Author(s):  
Lailiang Cheng ◽  
Shufu Dong ◽  
Leslie H. Fuchigami
Keyword(s):  
Leaf Area ◽  
N Uptake ◽  
Urea Solution ◽  
N Content ◽  
Wide Range ◽  
Leaf N Content ◽  
N Status ◽  
Foliar Urea ◽  
Whole Tree ◽  
Leaf N

Bench-grafted Fuji/M26 trees were fertigated with seven nitrogen concentrations (0, 2.5, 5.0, 7.5, 10, 15, and 20 mm) by using a modified Hoagland solution from 30 June to 1 Sept. In Mid-October, plants in each N treatment were divided into three groups. One group was destructively sampled to determine background tree N status before foliar urea application. The second group was painted with 3% 15N-urea solution twice at weekly interval on both sides of all leaves while the third group was left as controls. All the fallen leaves from both the 15N-treated and control trees were collected during the leaf senescence process and the trees were harvested after natural leaf fall. Nitrogen fertigation resulted in a wide range of tree N status in the fall. The percentage of whole tree N partitioned into the foliage in the fall increased linearly with increasing leaf N content up to 2.2 g·m–2, reaching a plateau of 50% to 55% with further rise in leaf N. 15N uptake and mobilization per unit leaf area and the percentage of 15N mobilized from leaves decreased with increasing leaf N content. Of the 15N mobilized back to the tree, the percentage of 15N partitioned into the root system decreased with increasing tree N status. Foliar 15N-urea application reduced the mobilization of existing N in the leaves regardless of leaf N status. More 15N was mobilized on a leaf area basis than that from existing N in the leaves with the low N trees showing the largest difference. On a whole-tree basis, the increase in the amount of reserve N caused by foliar urea treatment was similar. We conclude that low N trees are more effective in utilizing N from foliar urea than high N trees in the fall.

scholarly journals Whole-plant optimality predicts changes in leaf nitrogen under variable CO2 and nutrient availability

2019 ◽  
Author(s):  
Silvia Caldararu ◽  
Tea Thum ◽  
Lin Yu ◽  
Sönke Zaehle
Keyword(s):  
Plant Growth ◽  
Nutrient Limitation ◽  
Leaf Nitrogen ◽  
List Type ◽  
Carbon Export ◽  
N Content ◽  
Foliar N ◽  
Whole Plant ◽  
Leaf N Content ◽  
Leaf N

SummaryVegetation nutrient limitation is essential for understanding ecosystem responses to global change. In particular, leaf nitrogen (N) is known to be plastic under changed nutrient limitation. However, models can often not capture these observed changes, leading to erroneous predictions of whole-ecosystem stocks and fluxes.We hypothesise that an optimality approach can improve representation of leaf N content compared to existing empirical approaches. Unlike previous optimality-based approaches, which adjust foliar N concentrations based on canopy carbon export, we use a maximisation criteria based on whole-plant growth and allow for a lagged response of foliar N to this maximisation criterion to account for the limited plasticity of this plant trait. We test these model variants at a range of Free-Air CO2 Enrichment (FACE) and N fertilisation experimental sites.We show a model solely based on canopy carbon export fails to reproduce observed patterns and predicts decreasing leaf N content with increased N availability. However, an optimal model which maximises total plant growth can correctly reproduce the observed patterns.The optimality model we present here is a whole-plant approach which reproduces biologically realistic changes in leaf N and can thereby improve ecosystem-level predictions under transient conditions.

Heterogeneity of photosynthesis within leaves is associated with alteration of leaf structural features and leaf N content per leaf area in rice

2015 ◽  
Vol 42 (7) ◽  
pp. 687 ◽  
Author(s):  
Dongliang Xiong ◽  
Tingting Yu ◽  
Xi Liu ◽  
Yong Li ◽  
Shaobing Peng ◽  
...  
Keyword(s):  
Crop Yields ◽  
Biomass Accumulation ◽  
Structural Features ◽  
Leaf Nitrogen ◽  
Gas Exchange Parameters ◽  
N Content ◽  
Chlorophyll Contents ◽  
Functional Changes ◽  
Leaf N Content ◽  
Leaf N

Increasing leaf photosynthesis rate (A) is considered an important strategy to increase C3 crop yields. Leaf A is usually represented by point measurements, but A varies within each leaf, especially within large leaves. However, little is known about the effect of heterogeneity of A within leaves on rice performance. Here we investigated the changes in gas-exchange parameters and leaf structural and chemical features along leaf blades in two rice cultivars. Stomatal and mesophyll conductance as well as leaf nitrogen (N), Rubisco and chlorophyll contents increased from base to apex; consequently, A increased along leaves in both cultivars. The variation in A, leaf N content and Rubisco content within leaves was similar to the variations among cultivars, and the extent of A heterogeneity within leaves varied between cultivars, leading to different efficiencies of biomass accumulation. Furthermore, variation of A within leaves was closely associated with leaf structural and chemical features. Our findings emphasise that functional changes along leaf blades are associated with structural and chemical trait variation and that variation of A within leaves should be considered to achieve progress in future breeding programs.

Comparison of Machine Learning Methods for Leaf Nitrogen Estimation in Corn Using Multispectral UAV Images

2021 ◽  
Vol 64 (6) ◽  
pp. 2089-2101
Author(s):  
Razieh Barzin ◽  
Hamid Kamangir ◽  
Ganesh C. Bora
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Vegetation Indices ◽  
Leaf Nitrogen ◽  
Gradient Boosting ◽  
N Content ◽  
Spectral Bands ◽  
Red Edge ◽  
Leaf N Content ◽  
Leaf N

HighlightsLeaf nitrogen percentage in corn was estimated using various vegetation indices derived from UAVs.Eight machine learning methods were compared to find the most accurate model for nitrogen estimation.The most influential vegetation indices were determined for estimation of leaf nitrogen.Abstract. Nitrogen (N) is the most critical component of healthy plants. It has a significant impact on photosynthesis and plant reproduction. Physicochemical characteristics of plants such as leaf N content can be estimated spatially and temporally because of the latest developments in multispectral sensing technology and machine learning (ML) methods. The objective of this study was to use spectral data for leaf N estimation in corn to compare different ML models and find the best-fitted model. Moreover, the performance of vegetation indices (VIs) and spectral wavelengths were compared individually and collectively to determine if combinations of VIs substantially improved the results as compared to the original spectral data. This study was conducted at a Mississippi State University corn field that was divided into 16 plots with four different N treatments (0, 90, 180, and 270 kg ha-1). The bare soil pixels were removed from the multispectral images, and 26 VIs were calculated based on five spectral bands: blue, green, red, red-edge, and near-infrared (NIR). The 26 VIs and five spectral bands obtained from a red-edge multispectral sensor mounted on an unmanned aerial vehicle (UAV) were analyzed to develop ML models for leaf %N estimation of corn. The input variables used in these models had the most impact on chlorophyll and N content and high correlation with leaf N content. Eight ML algorithms (random forest, gradient boosting, support vector machine, multi-layer perceptron, ridge regression, lasso regression, and elastic net) were applied to three different categories of variables. The results show that gradient boosting and random forest were the best-fitted models to estimate leaf %N, with about an 80% coefficient of determination for the different categories of variables. Moreover, adding VIs to the spectral bands improved the results. The combination of SCCCI, NDRE, and red-edge had the largest coefficient of determination (R2) in comparison to the other categories of variables used to predict leaf %N content in corn. Keywords: Corn, Gradient boosting, Machine learning, Multispectral imagery, Nitrogen estimation, Random forest, UAV, Vegetation index.

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