The Effect of Low Irradiance on Leaf Nitrogen Allocation and Mesophyll Conductance to CO2 in Seedlings of Four Tree Species in Subtropical China

Low light intensity can lead to a decrease in photosynthetic capacity. However, could N-fixing species with higher leaf N contents mitigate the effects of low light? Here, we exposed seedlings of Dalbergia odorifera and Erythrophleum fordii (N-fixing trees), and Castanopsis hystrix and Betula alnoides (non-N-fixing trees) to three irradiance treatments (100%, 40%, and 10% sunlight) to investigate the effects of low irradiance on leaf structure, leaf N allocation strategy, and photosynthetic physiological parameters in the seedlings. Low irradiance decreased the leaf mass per unit area, leaf N content per unit area (Narea), maximum carboxylation rate (Vcmax), maximum electron transport rate (Jmax), light compensation point, and light saturation point, and increased the N allocation proportion of light-harvesting components in all species. The studied tree seedlings changed their leaf structures, leaf N allocation strategy, and photosynthetic physiological parameters to adapt to low-light environments. N-fixing plants had a higher photosynthesis rate, Narea, Vcmax, and Jmax than non-N-fixing species under low irradiance and had a greater advantage in maintaining their photosynthetic rate under low-radiation conditions, such as under an understory canopy, in a forest gap, or when mixed with other species.

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Whitefly, aphids and thrips attack on cabbage

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2006001000003 ◽

2006 ◽

Vol 41 (10) ◽

pp. 1469-1475 ◽

Cited By ~ 7

Author(s):

Germano Leão Demolin Leite ◽

Marcelo Picanço ◽

Gulab Newandram Jham ◽

Márcio Dionízio Moreira

Keyword(s):

Chemical Composition ◽

Relative Humidity ◽

Natural Enemies ◽

Leaf Nitrogen ◽

Significant Relation ◽

Total Rainfall ◽

Leaf N Content ◽

Cheiracanthium Inclusum ◽

Thrips Population ◽

Leaf N

The objective of this work was to investigate the relationships between predators and parasitoids, leaf chemical composition, levels of leaf nitrogen and potassium, total rainfall, relative humidity, daylight and median temperature on the intensity of whitefly, aphid, and thrips attack on cabbage. Whitefly, aphids and thrips population tended to proliferate in the final stage of plant or reached a peak population about 40 days after plantation. The whitefly and thrips tended to increase with an increase in the median temperature. A dependence of Cheiracanthium inclusum and Adialytus spp. populations on whitefly and aphids populations, respectively, was observed. No significant effect was detected between K and nonacosane leaf content and aphid population. However, an increase in leaf N content was followed by a decrease of this insect population. No significant relation was observed between leaf N, K and nonacosane and whitefly and thrips populations. Highest nonacosane levels were observed in plants 40 days after transplant, and relative humidity correlated negatively with nonacosane. Natural enemies, especially the parasitoid Adialytus spp. and the spiders can be useful controlling agents of the whitefly and aphids in cabbage. Median temperature can increase whitefly and thrips populations.

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Response of ‘Italian Riesling’ Leaf Nitrogen Status and Fruit Composition (Vitis vinifera L.) to Foliar Nitrogen Fertilization

HortScience ◽

10.21273/hortsci.51.3.262 ◽

2016 ◽

Vol 51 (3) ◽

pp. 262-267 ◽

Cited By ~ 1

Author(s):

Danijela Janjanin ◽

Marko Karoglan ◽

Mirjana Herak Ćustić ◽

Marijan Bubola ◽

Mirela Osrečak ◽

...

Keyword(s):

Amino Nitrogen ◽

Grape Juice ◽

Leaf Nitrogen ◽

Vitis Vinifera L ◽

Fruit Composition ◽

Yeast Assimilable Nitrogen ◽

Leaf N Content ◽

N Compounds ◽

N Status ◽

Leaf N

Two-year study was conducted on Italian Riesling cultivar with the aim to compare the effect of foliar sprays with different nitrogen forms on grapevine leaf N status, yield, and nitrogen compounds in grape juice. Treatments included no fertilization (control), soil NPK treatment, and three foliar treatments [amino acids, urea, ammonia (NH4+)/nitrate] applied four times during season, also treated with soil NPK. The application of 1% w/v urea significantly increased leaf total leaf N content in the second year of study. However, there were no effects on N compounds in grape juice, since changes in free amino nitrogen (FAN), NH4+, and consequently yeast assimilable nitrogen (YAN) were not consistent among the treatments and experimental years. Although increase of vine leaf N status was achieved by 1% w/v urea, additional modifying of application time (by moving it closer to veraison) is needed, with the aim to increase N compounds in grape juice as well.

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

10.1101/785329 ◽

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.

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

Functional Plant Biology ◽

10.1071/fp15057 ◽

2015 ◽

Vol 42 (7) ◽

pp. 687 ◽

Cited By ~ 16

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.

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

Transactions of the ASABE ◽

10.13031/trans.14305 ◽

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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Seedling leaves allocate lower fractions of nitrogen to photosynthetic apparatus in nitrogen fixing trees (Dalbergia odorifera and Erythrophleum fordii) than in non-nitrogen fixing trees (Betula alnoides and Castanopsis hystrix) in subtropical China

10.1101/482984 ◽

2018 ◽

Author(s):

Jingchao Tang ◽

Baodi Sun ◽

Ruimei Cheng ◽

Zuomin Shi ◽

Da Luo ◽

...

Keyword(s):

Cell Wall ◽

Photosynthetic Apparatus ◽

Water Volume ◽

Mesophyll Conductance ◽

Nitrogen Fixing ◽

Erythrophleum Fordii ◽

Dalbergia Odorifera ◽

Betula Alnoides ◽

Nitrogen Fixing Trees ◽

Leaf N

AbstractPhotosynthetic-nitrogen use efficiency (PNUE) is a useful trait to characterize leaf economics, physiology, and strategy. In this study, we investigated the differences in PNUE, leaf nitrogen (N) allocation, and mesophyll conductance (gm) in Dalbergia odorifera and Erythrophleum fordii (N-fixing trees), and Betula alnoides and Castanopsis hystrix (non-N-fixing trees). Seedlings of the four species were cultured in pots and received the same nutrient solution, water volume, and light. LiCor-6400 was used to determine fluorescence yield, photosynthetic response to light, and intercellular CO2 concentration (Ci). N allocation fractions in the photosynthetic apparatus were calculated according to Niinemets and Tenhunen method; gm was calculated according to variable J, EDO, and A-Ci curve fitting methods. PNUE of D. odorifera and E. fordii were significantly lower than those of B. alnoides and C. hystrix because of their allocation of a lower fraction of leaf N to Rubisco (PR) and bioenergetics (PB). Mesophyll conductance had a significant positive correlation with PNUE in D. odorifera, E. fordii, and B. alnoides. The fraction of leaf N to cell wall (PCW) had a significant negative correlation with PR in B. alnoides and C. hystrix. We conclude that B. alnoides and C. hystrix optimized their leaf N allocation toward photosynthesis, with the trade-off being N allocation to the cell wall and Rubisco. Thus, these two species may have a higher competitive ability in natural ecosystems with fertile soil.

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Leaf nitrogen allocation and partitioning in three groundwater-dependent herbaceous species in a hyper-arid desert region of north-western China

Australian Journal of Botany ◽

10.1071/bt11181 ◽

2012 ◽

Vol 60 (1) ◽

pp. 61 ◽

Cited By ~ 6

Author(s):

Jun-Tao Zhu ◽

Xiang-Yi Li ◽

Xi-Ming Zhang ◽

Qiang Yu ◽

Li-Sha Lin

Keyword(s):

Leaf Nitrogen ◽

Western China ◽

Alhagi Sparsifolia ◽

Use Efficiency ◽

Karelinia Caspica ◽

Arid Desert ◽

Leaf N Content ◽

North Western ◽

N Use ◽

Leaf N

Groundwater-dependent vegetation (GDV) is useful as an indicator of watertable depth and water availability in north-western China. Nitrogen (N) is an essential limiting resource for growth of GDV. To elucidate how leaf N allocation and partitioning influence photosynthesis and photosynthetic N-use efficiency (PNUE), three typical GDV species were selected, and their photosynthesis, leaf N allocation and partitioning were investigated in the Taklamakan Desert. The results showed that Karelinia caspica (Pall.) Less. and Peganum harmala L. had lower leaf N content, and allocated a lower fraction of leaf N to photosynthesis. However, they were more efficient in photosynthetic N partitioning among photosynthetic components. They partitioned a higher fraction of the photosynthetic N to carboxylation and showed higher PNUE, whereas Alhagi sparsifolia Shap. partitioned a higher fraction of the photosynthetic N to light-harvesting components. For K. caspica and P. harmala, the higher fraction of leaf N was allocated to carboxylation and bioenergetics, which led to a higher maximum net photosynthetic rate, and therefore to a higher PNUE, water-use efficiency (WUE), respiration efficiency (RE) and so on. In the desert, N and water are limiting resources; K. caspica and P. harmala can benefit from the increased PNUE and WUE. These physiological advantages and their higher leaf-area ratio (LAR) may contribute to their higher resource-capture ability.

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The Relationship between Leaf Nitrogen Content and Photosynthesis in Apple Leaves

HortScience ◽

10.21273/hortsci.31.4.578c ◽

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.

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Autumn Foliage Applications of ZnSO4 Reduced Leaf Nitrogen Remobilization in Peach and Nectarine

Journal of the American Society for Horticultural Science ◽

10.21273/jashs.115.1.79 ◽

1990 ◽

Vol 115 (1) ◽

pp. 79-83 ◽

Cited By ~ 7

Author(s):

S.P. Castagnoli ◽

T.M. DeJong ◽

S.A. Weinbaum ◽

R.S. Johnson

Keyword(s):

Leaf Senescence ◽

Prunus Persica ◽

Unit Area ◽

Dry Weight ◽

Leaf Nitrogen ◽

Nitrogen Remobilization ◽

Application Timing ◽

Area Basis ◽

N Remobilization ◽

Leaf N

Premature defoliation of peach and nectarine (Prunus persica L. Batsch) trees resulting from foliar applications of ZnSO4 reduced N remobilization that typically occurs during leaf senescence. Leaf N remobilization in unsprayed control trees ranged from 45% to 50%, irrespective of tree N status. Leaf N remobilization in trees receiving foliar applications of ZnSO4 ranged from a positive influx of N into the leaf to ≈30% of the N remobilized, depending on ZnSO4 application timing and method of expressing leaf N levels. Early ZnSO4 applications resulted in less N remobilization. Measuring leaf N on an area basis was a more precise indicator of N remobilization than N per unit dry weight, because leaf weight per unit area changes during leaf senescence.

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Leaf photosynthetic capacity and nitrogen content adjustment to canopy openness in tropical forest tree seedlings

Journal of Tropical Ecology ◽

10.1017/s0266467401001377 ◽

2001 ◽

Vol 17 (4) ◽

pp. 495-509 ◽

Cited By ~ 11

Author(s):

GRÉGOIRE VINCENT

Keyword(s):

Nitrogen Content ◽

Unit Area ◽

Light Level ◽

Forest Tree ◽

Leaf Nitrogen ◽

Canopy Openness ◽

Steady Increase ◽

Tree Seedlings ◽

Leaf Nitrogen Content ◽

Assimilation Rate

Maximum assimilation rate under saturating light level, leaf nitrogen and chlorophyll content and specific leaf mass were measured in forest plants grown under a range of canopy openness. Seedlings from three tree species co-occurring in damar agroforest (Sumatra) were examined: Shorea javanica, Lansium domesticum and Cinnamomum porrectum. A shrub species, Piper hispidum, growing in a different location was also investigated. All species showed pronounced differences in maximum photosynthetic potential when grown under different canopy openness. All tree seedlings showed an increase in maximum assimilation rate (Amax) with canopy openness (CO) until a certain threshold was reached. This saturation threshold varied between species. A steady increase in the maximum assimilation rate over the entire range of canopy openness explored was found only for Piper. Correlation between leaf nitrogen content and Amax was usually highest when expressed on a per unit area basis. However the overall correlation was poor whether expressed per unit mass or per unit area when all species were pooled together questioning the universality of the relationship between both quantities. Potential photosynthetic nitrogen use efficiency, defined as the amount of CO2 uptake per unit leaf nitrogen under saturating light level, was highest in Cinnamomum, supposedly the most light-demanding species, and lowest in Lansium, the understorey specialist.

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