Infection model for analyzing biological control of coffee rust using bacterial anti-fungal compounds

TL;DR

This paper develops a spatial stochastic model to analyze biological control of coffee rust using bacteria, offering an alternative to chemical fungicides with insights into stability, parameters, and control strategies.

Contribution

It introduces a novel spatial stochastic model for bacterial biological control of coffee rust, including analysis of equilibria, stability, and control strategies.

Findings

Model identifies conditions for stable control of coffee rust.

Parameter sensitivity analysis highlights key factors influencing effectiveness.

Numerical experiments demonstrate potential of biological control strategies.

Abstract

Coffee rust is one of the main diseases that affect coffee plantations worldwide. This causes an important economic impact in the coffee production industry in countries where coffee is an important part of the economy. A common method for combating this disease is using copper hydroxide as a fungicide, which can have damaging effects both on the coffee tree and on human health. A novel method for biological control of coffee rust using bacteria has been proven to be an effective alternative to copper hydroxide fungicides as anti-fungal compounds. In this paper, we develop and explore a spatial stochastic model for this interaction in a coffee plantation. We analyze equilibria for specific control strategies, as well as compute the basic reproductive number, R0, of individual coffee trees, conditions for local and global stability under specific conditions, parameter estimation of key parameters, as well as sensitivity analysis, and numerical experiments under local and global control strategies for key scenarios.