Simulation Model of a Tropical Foliar Epidemic Disease at Plant Scale: Case of Black Sigatoka on Banana


  • Clara Landry* UMR BGPI CIRAD- Guadeloupe
  • Francois Bonnot CIRAD, Umr BGPI, Montpellier
  • Virginie Ravigne CIRAD, Umr BGPI, Montpellier
  • Jean Carlier CIRAD, Umr BGPI, Montpellier
  • Jean Vaillant LAMIA, UAG, Guadeloupe
  • Catherine Abadie UMR BGPI CIRAD- Guadeloupe



Black sigatoka (BS), caused by the fungal pathogen В Mycosphaerella fijiensis, is considered as the most destructive foliar disease of banana andВ plantains. Controlling BS is essential to the export production because ofВ the important damages caused to fruit quality. The main current controlВ consists in frequent aerial fungicide applications and deleang, which is notВ a safe and durable solution. To overcome this practice, CIRAD has set up a banana breeding program to create BS partial resistant varieties. However evaluation of resistances ecacy puts constraints in time (long crop cycle) and space (numerous experimentals plots to set up).В To help in resistant hybrid selection, a mechanistic simulation model ofВ BS was designed. This model aims to better understand the pathosystemВ and to identify the most eective resistance components. The model was developed in discrete time at plant scale. It describes, without spatialization and in optimal climatic conditions, the development of the lesions during several crop cycles. Two sub-models are dened: the first one describes simply the growth of the banana in a deterministic way (9 parameters); the second one describes the complete and detailed epidemic cycle by integrating stochasticity (12 parameters).В Infectious cycle data were collected in both controlled and natural infestation conditions on susceptible and resistant cultivars. Data used forВ the model calibration were collected over a period of three months on theВ same kind of cultivars.В The estimation of the model parameters was realized in a bayesianВ framework using MCMC (Markov Chain of Monte Carlo) methods suchВ as the Metropolis-within-Gibbs algorithm.В First result of sensitivity analysis allow to quantify the epidemiologicalВ impact of each resistance components.






Conference Contributions