Leaf Nitrogen Status as a Main Contributor to Yield Improvement of Soybean Cultivars

2011 ◽  
Vol 103 (2) ◽  
pp. 441-448 ◽  
Author(s):  
Jian Jin ◽  
Xiaobing Liu ◽  
Guanghua Wang ◽  
Judong Liu ◽  
Liang Mi ◽  
...  
2011 ◽  
Vol 37 (6) ◽  
pp. 1039-1048 ◽  
Author(s):  
Fang-Yong WANG ◽  
Ke-Ru WANG ◽  
Shao-Kun LI ◽  
Shi-Ju GAO ◽  
Chun-Hua XIAO ◽  
...  

2018 ◽  
Vol 9 ◽  
Author(s):  
Songyang Li ◽  
Xingzhong Ding ◽  
Qianliang Kuang ◽  
Syed Tahir Ata-UI-Karim ◽  
Tao Cheng ◽  
...  

2007 ◽  
Vol 50 (5) ◽  
pp. 935-942 ◽  
Author(s):  
X. Yao ◽  
W. Feng ◽  
Y. Zhu ◽  
Y. C. Tian ◽  
W. X. Cao

1997 ◽  
Vol 37 (5) ◽  
pp. 599 ◽  
Author(s):  
R. A. Stephenson ◽  
E. C. Gallagher ◽  
V. J. Doogan

Summary. Despite the lack of evidence for a critical level of leaf nitrogen in macadamia, fertiliser management has been largely based on tentative standards for high yielding trees. Trees on a lower plane of nitrogen nutrition, however, produced higher yields of good quality nuts. This study was therefore carried out to establish the relationship between yield and nitrogen status of trees. Three rates of nitrogen fertiliser (0.5, 1.5 and 2.5 kg urea/tree . year; 230, 690 and 1150 g nitrogen respectively) were applied to macadamia trees in 1 of 5 application strategies: 1 application in April (floral initiation); 2 applications, one in April and one in June (inflorescence development); 3 applications, April, June and November (rapid nut growth and premature nut drop); 4 applications, April, June, November and January (oil accumulation); and 12 monthly split applications. Multiple applications were all equal in size. The association between high yields and low nitrogen status was confirmed. In some, but not all, years, yield was negatively correlated with leaf nitrogen, accounting for 47 and 59% of the variation in yield of commercially acceptable nuts (>19 mm diameter) in 1991 and 1993, respectively. It is therefore recommended that the standard for leaf nitrogen in macadamia be lowered from 1.4–1.5 to 1.3% under Australian conditions. These results raise concerns at the current trend for leaf nitrogen to be as high as 1.8%. It would be prudent to cease nitrogen applications on at least a small experimental block until leaf nitrogen declined to 1.3% and then maintain this level for at least 3 years and monitor yields.


Author(s):  
Wahono A. Wahono ◽  
D. Indradewa ◽  
B. H. Sunarminto ◽  
E. Haryono ◽  
D. Prajitno

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.


2004 ◽  
Vol 96 (1) ◽  
pp. 135 ◽  
Author(s):  
Lihong Xue ◽  
Weixing Cao ◽  
Weihong Luo ◽  
Tingbo Dai ◽  
Yan Zhu

Nitrogen ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 67-80
Author(s):  
Mohammad Habibullah ◽  
Mohammad Reza Mohebian ◽  
Raju Soolanayakanahally ◽  
Ali Newaz Bahar ◽  
Sally Vail ◽  
...  

A crop’s health can be determined by its leaf nutrient status; more precisely, leaf nitrogen (N) level, is a critical indicator that carries a lot of worthwhile nutrient information for classifying the plant’s health. However, the existing non-invasive techniques are expensive and bulky. The aim of this study is to develop a low-cost, quick-read multi-spectral sensor array to predict N level in leaves non-invasively. The proposed sensor module has been developed using two reflectance-based multi-spectral sensors (visible and near-infrared (NIR)). In addition, the proposed device can capture the reflectance data at 12 different wavelengths (six for each sensor). We conducted the experiment on canola leaves in a controlled greenhouse environment as well as in the field. In the greenhouse experiment, spectral data were collected from 87 leaves of 24 canola plants, subjected to varying levels of N fertilization. Later, 42 canola cultivars were subjected to low and high nitrogen levels in the field experiment. The k-nearest neighbors (KNN) algorithm was employed to model the reflectance data. The trained model shows an average accuracy of 88.4% on the test set for the greenhouse experiment and 79.2% for the field experiment. Overall, the result concludes that the proposed cost-effective sensing system can be viable in determining leaf nitrogen status.


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