Бюллетень Почвенного института им. В.В. Докучаева (Nov 2024)

Influence of Fe<sub>3</sub>O<sub>4</sub> nanoparticles on potato yield and development of soil microflora

  • N. A. Lyubimova,
  • G. Yu. Rabinovich

DOI
https://doi.org/10.19047/0136-1694-2024-SPYC-164-192
Journal volume & issue
Vol. 0, no. 0
pp. 164 – 192

Abstract

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The purpose of this work was to study both the separate and combined effects of the liquid-phase biological (LPB) product and Fe3O4 nanoparticles on the yield of potatoes of the Skarb variety, as well as on soil microflora. The biosynthesis of Fe3O4 nanoparticles was carried out using green tea extract and FeSO4∙7H2O solution, concentration 0.1 mol/l. The effectiveness of the obtained LPB-Fe product was studied under field conditions against the background of NPK fertilizers application. The results of a three-year experiment (2020–2022) showed that when spraying plants at vegetative stage with a 1% LPB-Fe product, potato yield increased by 16.9%, and when treating tubers before planting – by 14.8% compared to the control. At the same time, when using LPB without the addition of Fe3O4 nanoparticles, potato yield increased by 9.8% after foliar treatment and by 6.8% after tubers treatment, compared to the control. Based on the results of microbiological analysis, the coefficient of soil mineralization was calculated and the correlation of potato yield and its value was established. Varying concentrations of LPB-Fe preparation resulted in strong but multidirectional dependence of potato yield on the soil mineralization coefficient: both when treating tubers (regression equation y = 0.2639x – 39.9329 with a correlation coefficient r = 0.72) and when spraying potato plants (regression equation y = -0.2536x + 55.882 with correlation coefficient r = -0.77). In addition, during foliar treatment of potato plants with a 1% solution of Fe3O4 nanoparticles, there was recorded a very strong inverse relationship between the yield and the number of nitrogen-transforming microorganisms (correlation coefficient r = -0.90, with the regression equation y = -0.0841x + 37.9421).

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