Process-based modelling to predict the effects of climate change on myrtle rust

• Grant number: 23/15788-4
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: March 01, 2024
• End date: December 31, 2024
• Field of knowledge: Agronomical Sciences - Agronomy - Plant Health
• Principal Investigator: Lilian Amorim
• Grantee: Manoel Penachio Gonçalves
• Supervisor: Robert Marshall Beresford
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Institution abroad: The New Zealand Institute For Plant And Food Research Limited, New Zealand
• Associated to the scholarship: 22/02460-8 - Occurrence of guava rust in a scenario of temperature increase and water deficit imposed by global climate change, BP.DR

Abstract

Pathogen-process models have been developed to predict infection of Austropuccinia psidii, the causal agent of myrtle rust, also known as guava rust. However, disease development is greatly impacted by the environment and modeling the infection may not necessarily reflect the intensity of the disease. This BEPE project aims to model guava rust progress under different temperatures, in order to improve the previously described climatic risk model of myrtle rust. Guava rust progress under different temperature combinations were evaluated from experiments conducted in growth chambers in Brazil. These experiments were focused on heat stress, because estimates from the Intergovernmental Panel on Climate Change showed that the global average temperature will increase by 3.3 to 5.7°C by 2100. The data obtained from experiments carried out in Brazil will be analyzed and modeled to better understand the impacts of myrtle rust at future climate scenarios. The BEPE will be carried out at The New Zealand Institute for Plant & Food Research Ltd in Auckland with the supervision of Dr. Robert Beresford, a specialist in plant disease modeling and forecasting, over a period of 6 months. Dr. Robert Beresford has developed disease forecasting models and in recent years has been working on a climatic risk model to help analyze patterns of myrtle rust infection and predict the spread of the disease.