Control of mosaic disease using microbial biostimulants: insights from mathematical modelling

Blyuss, Konstantin B, Al Basir, Fahad, Tsygankova, Victoria A, Biliavska, Liudmyla O, Iutynska, Galyna O, Kyrychko, Serhii N, Dziuba, Serhii V, Tsyliuryk, Olexandr I and Izhboldin, Olexandr O (2020) Control of mosaic disease using microbial biostimulants: insights from mathematical modelling. Ricerche di Matematica. pp. 1-19. ISSN 0035-5038

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Abstract

A major challenge to successful crop production comes from viral diseases of plants that cause significant crop losses, threatening global food security and the livelihoods of countries that rely on those crops for their staple foods or source of income. One example of such diseases is a mosaic disease of plants, which is caused by begomoviruses and is spread to plants by whitefly. In order to mitigate negative impact of mosaic disease, several different strategies have been employed over the years, including roguing/replanting of plants, as well as using pesticides, which have recently been shown to be potentially dangerous to the environment and humans. In this paper we derive and analyse a mathematical model for control of mosaic disease using natural microbial biostimulants that, besides improving plant growth, protect plants against infection through a mechanism of RNA interference. By analysing the stability of the system’s steady states, we will show how properties of biostimulants affect disease dynamics, and in particular, how they determine whether the mosaic disease is eradicated or is rather maintained at some steady level. We will also present the results of numerical simulations that illustrate the behaviour of the model in different dynamical regimes, and discuss biological implications of theoretical results for the practical purpose of control of mosaic disease.

Item Type: Article
Schools and Departments: School of Mathematical and Physical Sciences > Mathematics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 20 May 2020 08:52
Last Modified: 20 May 2020 09:00
URI: http://sro.sussex.ac.uk/id/eprint/91321

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