title: Using virtual 3-D plant architecture to assess fungal pathogen splash dispersal in heterogeneous canopies: a case study with cultivar mixtures and a non-specialized disease causal agent.
abstract: OBJECTIVE
  Recent developments in plant disease management have led to a growing interest in alternative strategies, such as increasing host diversity and decreasing the use of pesticides. Use of cultivar mixtures is one option, allowing the spread of plant epidemics to be slowed down. As dispersal of fungal foliar pathogens over short distances by rain-splash droplets is a major contibutor to the spread of disease, this study focused on modelling the physical mechanisms involved in dispersal of a non-specialized pathogen within heterogeneous canopies of cultivar mixtures, with the aim of optimizing host diversification at the intra-field level.
  
  
  METHODS
  Virtual 3-D wheat-like plants (Triticum aestivum) were used to consider interactions between plant architecture and disease progression in heterogeneous canopies. A combined mechanistic and stochastic model, taking into account splash droplet dispersal and host quantitative resistance within a 3-D heterogeneous canopy, was developed. It consists of four sub-models that describe the spatial patterns of two cultivars within a complex canopy, the pathway of rain-splash droplets within this canopy, the proportion of leaf surface area impacted by dispersal via the droplets and the progression of disease severity after each dispersal event.
  
  
  RESULTS
  Different spatial organization, proportions and resistance levels of the cultivars of two-component mixtures were investigated. For the eight spatial patterns tested, the protective effect against disease was found to vary by almost 2-fold, with the greatest effect being obtained with the smallest genotype unit area, i.e. the ground area occupied by an independent unit of the host population that is genetically homogeneous. Increasing both the difference between resistance levels and the proportion of the most resistant cultivar often resulted in a greater protective effect; however, this was not observed for situations in which the most resistant of the two cultivars in the mixture had a relatively low level of resistance.
  
  
  CONCLUSIONS
  The results show agreement with previous data obtained using experimental approaches. They demonstrate that in order to maximize the potential mixture efficiency against a splash-dispersed pathogen, optimal susceptible/resistant cultivar proportions (ranging from 1/9 to 5/5) have to be established based on host resistance levels. The results also show that taking into account dispersal processes in explicit 3-D plant canopies can be a key tool for investigating disease progression in heterogeneous canopies such as cultivar mixtures.
authors.affiliation: INRA, UMR1091 Environnement et Grandes Cultures, F-78850 Thiverval-Grignon, France AgroParisTech, UMR Environnement et Grandes Cultures, F-78850 Thiverval-Grignon, France ARVALIS - Institut du végétal, Station expérimentale, F-91720 Boigneville, France.
authors.forename: C
authors.initials: C
authors.lastname: Gigot
authors.affiliation: INRA, UR1290 BIOGER-CPP, F-78850 Thiverval-Grignon, France.
authors.forename: C
authors.initials: C
authors.lastname: de Vallavieille-Pope
authors.affiliation: INRA, UMR1091 Environnement et Grandes Cultures, F-78850 Thiverval-Grignon, France AgroParisTech, UMR Environnement et Grandes Cultures, F-78850 Thiverval-Grignon, France.
authors.forename: L
authors.initials: L
authors.lastname: Huber
authors.affiliation: AgroParisTech, UMR Environnement et Grandes Cultures, F-78850 Thiverval-Grignon, France INRA, UMR1091 Environnement et Grandes Cultures, F-78850 Thiverval-Grignon, France Sebastien.Saint-Jean@AgroParisTech.fr.
authors.forename: S
authors.initials: S
authors.lastname: Saint-Jean
mesh_terms: D003198:Computer Simulation; D060467:Disease Resistance; D005658:Fungi / Q000502:physiology*; D054884:Host-Pathogen Interactions; D008954:Models, Biological; D010935:Plant Diseases / Q000382:microbiology*; D018515:Plant Leaves / Q000382:microbiology; D011891:Rain; D014908:Triticum / Q000276:immunology / Q000382:microbiology*
keywords: 3-D plant modelling; Functional–structural plant modelling; Triticum aestivum; cultivar mixture; fungal pathogen; intra-field diversity; mechanistic modelling; quantitative resistance; splash dispersal; wheat disease