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Magnetopriming effects on arsenic stress-induced morphological and physiological variations in soybean involving synchrotron imaging.

Anis FatimaSunita KatariaRajkumar PrajapatiMeeta JainAshish K AgrawalBalwant SinghYogesh KashyapDurgesh Kumar TripathiVijay Pratap SinghRekha Gadre
Published in: Physiologia plantarum (2020)
This study investigates the effect of static magnetic field (SMF) pre-treatment in ameliorating arsenic (As) toxicity in soybean plants in relation to growth, photosynthesis and water transport through leaf venation. Soybean (Glycine max variety JS-9560) seeds pre-treated with SMF (200 mT for 1 h) were grown in four levels of arsenate-polluted soil (As(V); 0, 5, 10 and 50 mg kg-1 ) in order to find out the impact of magnetopriming on plant tolerance against As toxicity. Quantitative image analysis of soybean leaf venation showed a narrowing in the width of midrib with increasing As(V) contamination in non-primed seeds. The morphological variations are also supported by the physiological parameters such as reduction in efficiency of photosystem II, plant performance index, stomatal conductance and photosynthetic rate in the presence of As(V) for non-primed seeds. However, remarkable increase was observed in all the measured parameters by SMF pre-treatment at all the concentrations of As(V) used. Even for the highest concentration of As(V) (50 mg kg-1 soil), SMF pre-treatment caused significant enhancement in plant height (40%), area of third trifoliate leaves (40%), along with increase in width of the midrib (17%) and minor vein (13%), contributing to increase in the water uptake, that resulted in higher primary photochemistry of PSII (12%), performance index (50%), stomatal conductance (57%) and photosynthetic rate (33%) as compared to non-primed ones. Consequently, magnetopriming of dry seeds can be effectively used as pretreatment for reduction of As toxicity in soybean plants.
Keyphrases
  • stress induced
  • drinking water
  • high resolution
  • heavy metals
  • machine learning
  • climate change
  • deep learning
  • mass spectrometry
  • oxide nanoparticles
  • electron transfer