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.

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 Whitefly, aphids and thrips attack on cabbage

2006 ◽  
Vol 41 (10) ◽  
pp. 1469-1475 ◽  
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.

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.

Variability in physiological responses of Venezuelan cacao to drought

2020 ◽  
Vol 56 (3) ◽  
pp. 407-421 ◽  
Author(s):  
Wilmer Tezara ◽  
Gabriela Pereyra ◽  
Eleinis Ávila-Lovera ◽  
Ana Herrera
Keyword(s):  
Chlorophyll Content ◽  
Water Use ◽  
Isotopic Ratio ◽  
Water Status ◽  
Common Garden ◽  
Photochemical Activity ◽  
Total Soluble Protein ◽  
N Content ◽  
Leaf N Content ◽  
Leaf N

AbstractIn order to assess the response of cocoa trees to drought, changes in water status, gas exchange, leaf carbon isotopic ratio (δ13C), photochemical activity, and leaf N and chlorophyll content during the rainy and dry season were measured in 31 Venezuelan cocoa clones (17 Trinitarios, 6 Criollos, and 8 Modern Criollos) grown in a common garden. Drought caused a 40% decrease in water potential (ψ) in all but the Modern Criollos, and a reduction in net photosynthetic rate (A) and stomatal conductance (gs) without an increase in instantaneous water use efficiency (WUE) in 93% of clones, and an increase in δ13C (long-term WUE) in 74% of clones; these responses suggest differences in tolerance to drought among clones. A positive correlation between A and both gs and leaf N content was found for all genotypes. Leaf N content, chlorophyll content, and photochemical activity were reduced during drought, suggesting that metabolism was also inhibited. The best performance during drought was shown by Modern Criollos with the highest WUE, while five Trinitario clones seemed to be less sensitive to drought, since neither chlorophyll, N, total soluble protein concentration, nor gs changed with drought, indicating that those Trinitario clones, with lower A, have a conservative water use. Modern Criollos showed no reductions in either ψ or gs; A remained unchanged, as did WUE, which was the highest, suggesting that these clones would be more successful in environments with low water availability. Our results indicate large variation in physiological response to drought over a range of parameters, suggesting possible differences in tolerance among clones.

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