| Literature DB >> 33929880 |
Loup Rimbaud1,2, Frédéric Fabre3, Julien Papaïx4, Benoît Moury1, Christian Lannou5, Luke G Barrett2, Peter H Thrall2.
Abstract
Owing to their evolutionary potential, plant pathogens are able to rapidly adapt to genetically controlled plant resistance, often resulting in resistance breakdown and major epidemics in agricultural crops. Various deployment strategies have been proposed to improve resistance management. Globally, these rely on careful selection of resistance sources and their combination at various spatiotemporal scales (e.g., via gene pyramiding, crop rotations and mixtures, landscape mosaics). However, testing and optimizing these strategies using controlled experiments at large spatiotemporal scales are logistically challenging. Mathematical models provide an alternative investigative tool, and many have been developed to explore resistance deployment strategies under various contexts. This review analyzes 69 modeling studies in light of specific model structures (e.g., demographic or demogenetic, spatial or not), underlying assumptions (e.g., whether preadapted pathogens are present before resistance deployment), and evaluation criteria (e.g., resistance durability, disease control, cost-effectiveness). It highlights major research findings and discusses challenges for future modeling efforts. Expected final online publication date for the Annual Review of Phytopathology, Volume 59 is August 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.Year: 2021 PMID: 33929880 DOI: 10.1146/annurev-phyto-020620-122134
Source DB: PubMed Journal: Annu Rev Phytopathol ISSN: 0066-4286 Impact factor: 13.078