Chicory is an important medicinal plant and also used as a leaf vegetable for pasture forage. In order to study the effect of nitrogen and pant density on this plant, an experiment was conducted in the 2009-2010 growing season at the Agricultural Training Center of Mohammadieh, Birjand, Iran. A split plot experiment based on a randomized complete block design with three replications was used. Nitrogen rates including of 0, 100 and 200 kg.ha -1 N as urea (N0, N100 and N200, respectively) were arranged in main plots and four planting density (25, 16.7, 12.5 and 10 plants.m -2 ) were employed in the subplots. The results indicated that nitrogen application had not any significant effect on dry root yield, but application of 200 kg.ha -1 N compared to control, increased dry leaf and total yields 39.85 and 31.89 percent, respectively. Although nitrogen application significantly decreased NUE but increasing plant density increased it. Increasing plant density enhanced root and leaf dry yield, but declined plant height, axillary root number and root length. Nitrogen application had not any significant effect on axillary root number and root diameter and length. The effect of nitrogen and plant density on root/shoot ratio was not significant. Totally the result showed that chicory's yield respond positively to increasing plant density (25 plant.m -2 ) and nitrogen rate (200 kg.ha -1 ) but increasing the yield was not proportionally to nitrogen fertilizer, consequently NUE declined in the highest nitrogen treatment.
Inverse synthetic aperture radar (ISAR) provides a solution to increase the radar angular resolution by observing a moving target over time. The high-resolution ISAR image should undergo a segmentation step to get the target’s point cloud data, which is then used for classification purposes. Existing segmentation algorithms seek an optimal threshold in an iterative manner, which adds to the complexity of ISAR and results in an increase in the processing time. In this paper, we take advantage of the distribution of the ISAR image intensity, which is based on the Rayleigh distribution, and obtain an explicit relationship for the optimal segmentation threshold. The proposed segmentation algorithm alleviates the requirement for iterative optimization and its efficiency is shown using both simulated and experimental ISAR images.
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