scholarly journals Comparative Nitrogen Uptake and Distribution in Corn and Velvetleaf (Abutilon theophrasti)

Weed Science ◽  
2007 ◽  
Vol 55 (2) ◽  
pp. 102-110 ◽  
Author(s):  
John L. Lindquist ◽  
Darren C. Barker ◽  
Stevan Z. Knezevic ◽  
Alexander R. Martin ◽  
Daniel T. Walters
Keyword(s):  
N Uptake ◽  
Soil Nutrient ◽  
Biomass Accumulation ◽  
Field Studies ◽  
Total Biomass ◽  
N Addition ◽  
N Content ◽  
Area Index ◽  
Leaf N Content ◽  
Leaf N

Weeds compete with crops for light, soil water, and nutrients. Nitrogen (N) is the primary limiting soil nutrient. Forecasting the effects of N on growth, development, and interplant competition requires accurate prediction of N uptake and distribution within plants. Field studies were conducted in 1999 and 2000 to determine the effects of variable N addition on monoculture corn and velvetleaf N uptake, the relationship between plant N concentration ([N]) and total biomass, the fraction of N partitioned to leaves, and predicted N uptake and leaf N content. Cumulative N uptake of both species was generally greater in 2000 than in 1999 and tended to increase with increasing N addition. Corn and velvetleaf [N] declined with increasing biomass in both years in a predictable manner. The fraction of N partitioned to corn and velvetleaf leaves varied with thermal time from emergence but was not influenced by year, N addition, or weed density. With the use of the [N]–biomass relationship to forecast N demand, cumulative corn N uptake was accurately predicted for three of four treatments in 1999 but was underpredicted in 2000. Velvetleaf N uptake was accurately predicted in all treatments in both years. Leaf N content (NL, g N m−2leaf) was predicted by the fraction of N partitioned to leaves, predicted N uptake, and observed leaf area index for each species. Average deviations between predicted and observed corn NLwere < 88 and 12% of the observed values in 1999 and 2000, respectively. Velvetleaf NLwas less well predicted, with average deviations ranging from 39 to 248% of the observed values. Results of this research indicate that N uptake in corn and velvetleaf was driven primarily by biomass accumulation. Overall, the approaches outlined in this paper provide reasonable predictions of corn and velvetleaf N uptake and distribution in aboveground tissues.

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.

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.

scholarly journals Spatial Distribution of Leaf Area Index and Leaf N Content In Relation To Grain Yield and Nitrogen Uptake in Rice

2007 ◽  
Vol 10 (1) ◽  
pp. 136-145 ◽  
Author(s):  
Qi Jing ◽  
Tingbo Dai ◽  
Dong Jiang ◽  
Yan Zhu ◽  
Weixing Cao
Keyword(s):  
Spatial Distribution ◽  
Grain Yield ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Nitrogen Uptake ◽  
N Content ◽  
Area Index ◽  
Leaf N Content ◽  
Leaf N

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 Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 314
Author(s):  
Andrew Revill ◽  
Vasileios Myrgiotis ◽  
Anna Florence ◽  
Stephen Hoad ◽  
Robert Rees ◽  
...  
Keyword(s):  
Field Experiments ◽  
Process Models ◽  
Agricultural Technologies ◽  
N Application ◽  
Area Index ◽  
Complex Process ◽  
C Cycle ◽  
The Mean ◽  
Leaf N Content ◽  
Leaf N

Climate, nitrogen (N) and leaf area index (LAI) are key determinants of crop yield. N additions can enhance yield but must be managed efficiently to reduce pollution. Complex process models estimate N status by simulating soil-crop N interactions, but such models require extensive inputs that are seldom available. Through model-data fusion (MDF), we combine climate and LAI time-series with an intermediate-complexity model to infer leaf N and yield. The DALEC-Crop model was calibrated for wheat leaf N and yields across field experiments covering N applications ranging from 0 to 200 kg N ha−1 in Scotland, UK. Requiring daily meteorological inputs, this model simulates crop C cycle responses to LAI, N and climate. The model, which includes a leaf N-dilution function, was calibrated across N treatments based on LAI observations, and tested at validation plots. We showed that a single parameterization varying only in leaf N could simulate LAI development and yield across all treatments—the mean normalized root-mean-square-error (NRMSE) for yield was 10%. Leaf N was accurately retrieved by the model (NRMSE = 6%). Yield could also be reasonably estimated (NRMSE = 14%) if LAI data are available for assimilation during periods of typical N application (April and May). Our MDF approach generated robust leaf N content estimates and timely yield predictions that could complement existing agricultural technologies. Moreover, EO-derived LAI products at high spatial and temporal resolutions provides a means to apply our approach regionally. Testing yield predictions from this approach over agricultural fields is a critical next step to determine broader utility.

scholarly journals Effect of nitrogen addition on the comparative productivity of corn and velvetleaf (Abutilon theophrasti)

Weed Science ◽  
2006 ◽  
Vol 54 (02) ◽  
pp. 354-363 ◽  
Author(s):  
Darren C. Barker ◽  
Stevan Z. Knezevic ◽  
Alex R. Martin ◽  
Daniel T. Walters ◽  
John L. Lindquist
Keyword(s):  
Leaf Area ◽  
Field Experiments ◽  
Yield Loss ◽  
Biomass Accumulation ◽  
Nitrogen Addition ◽  
Corn Yield ◽  
N Addition ◽  
Area Index ◽  
Nitrogen Levels ◽  
Weed Growth

Weeds that respond more to nitrogen fertilizer than crops may be more competitive under high nitrogen (N) conditions. Therefore, understanding the effects of nitrogen on crop and weed growth and competition is critical. Field experiments were conducted at two locations in 1999 and 2000 to determine the influence of varying levels of N addition on corn and velvetleaf height, leaf area, biomass accumulation, and yield. Nitrogen addition increased corn and velvetleaf height by a maximum of 15 and 68%, respectively. N addition increased corn and velvetleaf maximum leaf area index (LAI) by up to 51 and 90%. Corn and velvetleaf maximum biomass increased by up to 68 and 89% with N addition. Competition from corn had the greatest effect on velvetleaf growth, reducing its biomass by up to 90% compared with monoculture velvetleaf. Corn response to N addition was less than that of velvetleaf, indicating that velvetleaf may be most competitive at high levels of nitrogen and least competitive when nitrogen levels are low. Corn yield declined with increasing velvetleaf interference at all levels of N addition. However, corn yield loss due to velvetleaf interference was similar across N treatments except in one site–year, where yield loss increased with increasing N addition. Corn yield loss due to velvetleaf interference may increase with increasing N supply when velvetleaf emergence and early season growth are similar to that of corn.

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 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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