ArticleEvolutionary applications2018
Mosaics, mixtures, rotations or pyramiding: What is the optimal strategy to deploy major gene resistance?
Article in Evolutionary applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Adaptation ofEvolutionary applications · 2025Article
- Modelling the Effectiveness of Gene-Edited Salmon at Sea Lice Control and the Use of Refugia to Mitigate Counter-Adaptation.Evolutionary applications · 2025Article
- A Simplified and Integrated View of Disease Control in Varietal Mixtures Using the Phytobiome Framework.Plant, cell & environment · 2025Review
- Quantitative Resistance Deployment Can Strengthen Epidemics in Perennial Plants by Selecting Maladapted Pathogen Strains.Evolutionary applications · 2025Article
- Optimizing Clubroot Management and the Role of Canola Cultivar Mixtures.Pathogens (Basel, Switzerland) · 2024Review
- Evolution and spread of multiadapted pathogens in a spatially heterogeneous environment.Evolution letters · 2024Article
- Building pyramids against the evolutionary emergence of pathogens.Proceedings. Biological sciences · 2024Article
- Impact of ontogenic changes on the dynamics of a fungal crop disease model motivated by coffee leaf rust.Journal of mathematical biology · 2024Review
- Effects of pathogen reproduction system on the evolutionary and epidemiological control provided by deployment strategies for two major resistance genes in agricultural landscapes.Evolutionary applications · 2024Article
- Evolutionary predictions for a parasite metapopulation: Modelling salmon louse resistance to pest controls in aquaculture.Evolutionary applications · 2023Article
- Why Do We Need Alternative Methods for Fungal Disease Management in Plants?Plants (Basel, Switzerland) · 2023Review
- The socio-economic challenges of managing pathogen evolution in agriculture.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- Understanding the effect of component proportions on disease control in two-component cultivar cereal mixtures using a pathogen dispersal scaling hypothesis.Scientific reports · 2023Article
- Applying genetic technologies to combat infectious diseases in aquaculture.Reviews in aquaculture · 2023Review
- Importance of interindividual interactions in eco-evolutionary population dynamics: The rise of demo-genetic agent-based models.Evolutionary applications · 2022Article
- Delaying quantitative resistance to pesticides and antibiotics.Evolutionary applications · 2022Article
- A cool climate perspective on grapevine breeding: climate change and sustainability are driving forces for changing varieties in a traditional market.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Review
- Long-term blast control in high eating quality rice using multilines.Scientific reports · 2022Article
- Genetic diversity and disease: The past, present, and future of an old idea.Evolution; international journal of organic evolution · 2022Article
- Gene drive strategies of pest control in agricultural systems: Challenges and opportunities.Evolutionary applications · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Once deployed uniformly in the field, genetically controlled plant resistance is often quickly overcome by pathogens, resulting in dramatic losses. Several strategies have been proposed to constrain the evolutionary potential of pathogens and thus increase resistance durability. These strategies can be classified into four categories, depending on whether resistance sources are varied across time (rotations) or combined in space in the same cultivar (pyramiding), in different cultivars within a field (cultivar mixtures) or among fields (mosaics). Despite their potential to differentially affect both pathogen epidemiology and evolution, to date the four categories of deployment strategies have never been directly compared together within a single theoretical or experimental framework, with regard to efficiency (ability to reduce disease impact) and durability (ability to limit pathogen evolution and delay resistance breakdown). Here, we used a spatially explicit stochastic demogenetic model, implemented in the R package
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