Physiological attributes of tomato (Lycopersicon esculentum Mill.) influenced by different sources of nutrients at foothill of eastern Himalayan region
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چکیده
منابع مشابه
Growth and Physiological Responses of Tomato (lycopersicon Esculentum Mill.) to Simulated Acid Rain
This investigation was undertaken to ascertain the effect of simulated acid rain (SAR) on growth, yield and physiological parameters of tomato. SAR exposure (pH 3.0 and 4.0) caused white-to-tan spots on the abaxial and adaxial surface of tomato leaves. SAR exposure at pH 3.0 and 4.0 significantly suppressed pigment synthesis, shoot and root dry weights and yield of tomato. The effects were more...
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In vitro flowering and fruiting were induced in plants regenerated from calli of leaf explants of Lycopersicon esculentum var. Pant 11. MS medium supplemented with 2 mg/l BAP displayed the best response followed by MS with 2 mg/l BAP, 1 mg/ l ABA and 0.5 mg/l IAA. Flower buds were developed both directly from callus and regenerated plantlets from leaf explants under 16 h (39.3 μmol m-2s-1) phot...
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The postharvest quality of tomato fruit harvested at the pink stage and dipped in 3 different concentrations of CaCl2 for different dip times (30, 20 and 10 minutes) were studied. Tomato fruits dipped in 6% CaCl2 for 30 and 20 minutes retained significantly (P < 0.05) higher level of firmness, titrable acidity and vitamin C than fruits dipped in 2% CaCl2 and the control. The calcium chloride tr...
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An experiment on the effect of different sources of Arbuscular mycorrhiza (AM) fungi on tomato seedlings (var. Roma VF) was conducted at the Bangladesh Agricultural Research Institute, Gazipur during rabi 2007-08 and 2008-09. Eight sources of AM fungi viz., AM-01 (Jessore), AM-02 (Rahmatpur), AM-03 (Joydebpur), AM-04 (Ullapara), AM-05 (Jamalpur), AM-06 (Hathazari), AM-07 (Ishurdi), and AM-08 (R...
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a problem of computer vision applications is to detect regions of interest under dif- ferent imaging conditions. the state-of-the-art maximally stable extremal regions (mser) detects affine covariant regions by applying all possible thresholds on the input image, and through three main steps including: 1) making a component tree of extremal regions’ evolution (enumeration), 2) obtaining region ...
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ژورنال
عنوان ژورنال: Journal of Applied and Natural Science
سال: 2013
ISSN: 2231-5209,0974-9411
DOI: 10.31018/jans.v5i2.317