scholarly journals Effects of Canopy Light Distribution Characteristics and Leaf Nitrogen Content on Efficiency of Radiation Use in Dry Matter Accumulation of Soybean Cultivars.

1994 ◽  
Vol 63 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Tatsuhiko SHIRAIWA ◽  
Ushio HASHIKAWA ◽  
Shingo TAKA ◽  
Ayako SAKAI
Keyword(s):  
Nitrogen Content ◽  
Dry Matter ◽  
Leaf Nitrogen ◽  
Light Distribution ◽  
Leaf Nitrogen Content ◽  
Dry Matter Accumulation ◽  
Distribution Characteristics ◽  
Soybean Cultivars ◽  
Radiation Use

scholarly journals Leaf Nitrogen Content, Photosynthesis and Radiation Use Efficiency in Peanut1

1993 ◽  
Vol 20 (1) ◽  
pp. 40-43 ◽  
Author(s):  
T. R. Sinclair ◽  
J. M. Bennett ◽  
K. J. Boote
Keyword(s):  
Nitrogen Content ◽  
Field Tests ◽  
Positive Association ◽  
Growing Season ◽  
Leaf Nitrogen ◽  
Radiation Use Efficiency ◽  
Leaf Nitrogen Content ◽  
Wide Range ◽  
Use Efficiency ◽  
Radiation Use

Abstract It has been hypothesized that a close correlation exists between specific leaf nitrogen content (SLN, g N m-2 leaf area) and leaf carbon exchange rate (CER), and crop radiation use efficiency (RUE). This association has not been investigated previously in peanut (Arachis hypogaea L.) so the objective of this research was to obtain such data under greenhouse and field conditions. In the greenhouse study differing nitrogen fertilizer treatments for a non-nodulated cultivar resulted in leaves with a wide range of SLN and CER. A strong, positive association between SLN and CER was found. In the field little variation in either SLN or CER was observed through much of the growing season in four commercial cultivars. Consistent with the observation of stability in SLN and CER, RUE based on total, intercepted solar radiation was found to be constant at 1.00 g MJ-1 through the growing season. However, the observed RUE was 29% greater than a theoretical RUE calculated assuming a uniform distribution of SLN in the canopy. One possibility is that RUE of peanuts may be enhanced by a nonuniform SLN distribution within its leaf canopy. In any event, the results of both the greenhouse and field tests showed that peanut CO2 assimilation is closely linked to leaf SLN.

scholarly journals Effect of split and foliar application of nitrogen on leaf nitrogen concentration, SPAD index and photosynthesis in Bt. Cotton (Gossypium hirsutum L.)

2017 ◽  
Vol 14 (2) ◽  
pp. 1-11
Author(s):  
MD Giri ◽  
MB Dhonde ◽  
AD Tumbare
Keyword(s):  
Gossypium Hirsutum ◽  
Nitrogen Content ◽  
Foliar Application ◽  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Bt Cotton ◽  
Leaf Nitrogen Content ◽  
Dry Matter Accumulation ◽  
Gossypium Hirsutum L ◽  
Leaf Nitrogen Concentration

A field experiment was carried out at Rahuri (Maharashtra) India in consecutive years of 2011 to 2012 to study the response of split and foliar application of nitrogen on leaf nitrogen concentration, SPAD index and photosynthesis in Bt cotton (Gossypium hirsutum L). Ten treatments comprising nitrogen–management practices (application of recommended dose of nitrogen in 3, 4, 5, 6 splits; foliar application of 20 g KNO3 litre-1 water, 20 g urea litre-1 water, combination of split application of nitrogen and foliar spray of 20 g KNO3 litre-1 water, 20 g urea litre-1 water and control) were tested on inceptisol. Results indicated that application of nitrogen in six splits (20% at sowing as basal and remaining in 5 equal splits at 30, 45, 60, 75 and 90 DAS) registered significantly higher leaf nitrogen content, SPAD index and rate of photosynthesis except 60 DAS where 4 nitrogen split showed higher leaf nitrogen content and SPAD index. Similarly the same treatment registered significantly higher plant height, dry matter accumulation, number of bolls plant-1 and lint yield.SAARC J. Agri., 14(2): 1-11 (2016)

scholarly journals Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat

2021 ◽  
Vol 13 (4) ◽  
pp. 739
Author(s):  
Jiale Jiang ◽  
Jie Zhu ◽  
Xue Wang ◽  
Tao Cheng ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Precision Agriculture ◽  
Field Experiments ◽  
Mean Squared Error ◽  
Growing Season ◽  
Leaf Nitrogen ◽  
Textural Analysis ◽  
Hyperspectral Images ◽  
Leaf Nitrogen Content ◽  
New Feature

Real-time and accurate monitoring of nitrogen content in crops is crucial for precision agriculture. Proximal sensing is the most common technique for monitoring crop traits, but it is often influenced by soil background and shadow effects. However, few studies have investigated the classification of different components of crop canopy, and the performance of spectral and textural indices from different components on estimating leaf nitrogen content (LNC) of wheat remains unexplored. This study aims to investigate a new feature extracted from near-ground hyperspectral imaging data to estimate precisely the LNC of wheat. In field experiments conducted over two years, we collected hyperspectral images at different rates of nitrogen and planting densities for several varieties of wheat throughout the growing season. We used traditional methods of classification (one unsupervised and one supervised method), spectral analysis (SA), textural analysis (TA), and integrated spectral and textural analysis (S-TA) to classify the images obtained as those of soil, panicles, sunlit leaves (SL), and shadowed leaves (SHL). The results show that the S-TA can provide a reasonable compromise between accuracy and efficiency (overall accuracy = 97.8%, Kappa coefficient = 0.971, and run time = 14 min), so the comparative results from S-TA were used to generate four target objects: the whole image (WI), all leaves (AL), SL, and SHL. Then, those objects were used to determine the relationships between the LNC and three types of indices: spectral indices (SIs), textural indices (TIs), and spectral and textural indices (STIs). All AL-derived indices achieved more stable relationships with the LNC than the WI-, SL-, and SHL-derived indices, and the AL-derived STI was the best index for estimating the LNC in terms of both calibration (Rc2 = 0.78, relative root mean-squared error (RRMSEc) = 13.5%) and validation (Rv2 = 0.83, RRMSEv = 10.9%). It suggests that extracting the spectral and textural features of all leaves from near-ground hyperspectral images can precisely estimate the LNC of wheat throughout the growing season. The workflow is promising for the LNC estimation of other crops and could be helpful for precision agriculture.

scholarly journals Leaf traits and gas exchange in saplings of native tree species in the Central Amazon

2010 ◽  
Vol 67 (6) ◽  
pp. 624-632 ◽  
Author(s):  
Keila Rego Mendes ◽  
Ricardo Antonio Marenco
Keyword(s):  
Gas Exchange ◽  
Nitrogen Content ◽  
Leaf Area ◽  
Tree Species ◽  
Leaf Traits ◽  
Leaf Nitrogen ◽  
Dry Season ◽  
Leaf Thickness ◽  
Rainfall Regime ◽  
Leaf Nitrogen Content

Global climate models predict changes on the length of the dry season in the Amazon which may affect tree physiology. The aims of this work were to determine the effect of the rainfall regime and fraction of sky visible (FSV) at the forest understory on leaf traits and gas exchange of ten rainforest tree species in the Central Amazon, Brazil. We also examined the relationship between specific leaf area (SLA), leaf thickness (LT), and leaf nitrogen content on photosynthetic parameters. Data were collected in January (rainy season) and August (dry season) of 2008. A diurnal pattern was observed for light saturated photosynthesis (Amax) and stomatal conductance (g s), and irrespective of species, Amax was lower in the dry season. However, no effect of the rainfall regime was observed on g s nor on the photosynthetic capacity (Apot, measured at saturating [CO2]). Apot and leaf thickness increased with FSV, the converse was true for the FSV-SLA relationship. Also, a positive relationship was observed between Apot per unit leaf area and leaf nitrogen content, and between Apot per unit mass and SLA. Although the rainfall regime only slightly affects soil moisture, photosynthetic traits seem to be responsive to rainfall-related environmental factors, which eventually lead to an effect on Amax. Finally, we report that little variation in FSV seems to affect leaf physiology (Apot) and leaf anatomy (leaf thickness).

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