scholarly journals Cattle Horn Shavings: A Possible Nitrogen Source for Apple Trees

Agronomy ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 540
Author(s):  
Juozas Lanauskas ◽  
Nobertas Uselis ◽  
Loreta Buskienė ◽  
Romas Mažeika ◽  
Gediminas Staugaitis ◽  
...  
Keyword(s):  
Ammonium Nitrate ◽  
Nitrogen Content ◽  
Nitrogen Nutrition ◽  
Agricultural Waste ◽  
Leaf Nitrogen ◽  
Mineral Nitrogen ◽  
Research Centre ◽  
Leaf Nitrogen Content ◽  
Apple Trees ◽  
Orchard Soil

The circular economy concept promotes the recycling of agricultural waste. This study was aimed at investigating the effects of cattle horn shavings on apple tree nitrogen nutrition. Ligol apple trees on P 60 rootstock were the object of the study. The experiment was conducted in the experimental orchard of the Institute of Horticulture, Lithuanian Research Centre for Agriculture and Forestry, from 2015 to 2018. Two fertiliser rates were tested: 50 and 100 kg/ha N. Horn shavings (14.1% N) were applied at the end of autumn or at the beginning of vegetation in the spring and in one treatment 100 kg/ha N rate was divided into two equal parts and applied both in autumn and spring. The effects of the horn shavings were compared with the effects of ammonium nitrate (34.4% N) and the unfertilised treatment. The lowest mineral nitrogen content was found in the unfertilised orchard soil and the soil fertilised with horn shavings in the spring at 50 kg/ha N equivalent. In all other cases, the fertilisers increased the soil’s mineral nitrogen content. The lowest leaf nitrogen content was found in apple trees that grew in the unfertilised orchard soil or soil fertilised in the spring with 50 kg/ha N of horn shavings (1.58–2.13%). In other cases, leaf nitrogen content was higher (1.77–2.17%). The apple trees with the lowest leaf nitrogen content produced the smallest average yield (34.5–36.6 t/ha). The highest yield was recorded from fruit trees fertilised with 50 kg/ha N of ammonium nitrate applied in spring or horn shavings applied in autumn (42.4 and 41.4 t/ha, respectively). The influence of horn shavings on the other studied parameters was similar to that of ammonium nitrate. Horn shavings, like nitrogen fertiliser, could facilitate nitrogen nutrition management in apple trees, especially in organic orchards, where the use of synthetic fertilisers is prohibited.

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).

Improvement of leaf nitrogen content inference in Valencia-orange trees applying spectral analysis algorithms in UAV mounted-sensor images

2019 ◽  
Vol 83 ◽  
pp. 101907 ◽  
Author(s):  
Lucas Prado Osco ◽  
Ana Paula Marques Ramos ◽  
Érika Akemi Saito Moriya ◽  
Maurício de Souza ◽  
José Marcato Junior ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Nitrogen Content ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Content ◽  
Valencia Orange ◽  
Orange Trees

scholarly journals Estimation of Leaf Nitrogen Content from Spectral Characteristics of Rice Canopy

2001 ◽  
Vol 1 ◽  
pp. 81-89 ◽  
Author(s):  
Chwen-Ming Yang
Keyword(s):  
Nitrogen Content ◽  
Linear Relationship ◽  
Spectral Reflectance ◽  
Spectral Characteristics ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Content ◽  
Nitrogen Application ◽  
Spectral Signatures ◽  
Growing Period ◽  
Application Rates

Ground-based remotely sensed reflectance spectra of hyperspectral resolution were monitored during the growing period of rice under various nitrogen application rates. It was found that reflectance spectrum of rice canopy changed in both wavelength and reflectance as the plants developed. Fifteen characteristic wavebands were identified from the apparent peaks and valleys of spectral reflectance curves, in accordance with the results of the first-order differentiation, measured over the growing season of rice. The bandwidths and center wavelengths of these characteristic wavebands were different among nitrogen treatments. The simplified features by connecting these 15 characteristic wavelengths may be considered as spectral signatures of rice canopy, but spectral signatures varied with developmental age and nitrogen application rates. Among these characteristic wavebands, the changes of the wavelength in band 11 showed a positive linear relationship with application rates of nitrogen fertilizer, while it was a negative linear relationship in band 5. Mean reflectance of wavelengths in bands 1, 2, 3, 5, 11, and 15 was significantly correlated with application rates. Reflectance of these six wavelengths changed nonlinearly after transplanting and could be used in combination to distinguish rice plants subjected to different nitrogen application rates. From the correlation analyses, there are a variety of correlation coefficients for spectral reflectance to leaf nitrogen content in the range of 350-2400 nm. Reflectance of most wavelengths exhibited an inverse correlation with leaf nitrogen content, with the largest negative value (r = �0.581) located at about 1376 nm. Changes in reflectance at 1376 nm to leaf nitrogen content during the growing period were closely related and were best fitted to a nonlinear function. This relationship may be used to estimate and to monitor nitrogen content of rice leaves during rice growth. Reflectance of red light minimum and near-infrared peak and leaf nitrogen content were correlated nonlinearly.

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