Development stage-dependent susceptibility of cocoa fruit to pod rot caused by Phytophthora megakarya
- 277 Downloads
Pod rot causes up to 30 % losses in world cocoa production. In order to predict the risk evolution of disease, it is important to take into consideration the developmental stage of fruits. In fact, it has been shown that the risk of attack by pod rot depends amongst others on the developmental stage of fruits. We proposed here to estimate the susceptibility at different stages. Susceptibility of fruit to disease was investigated at three fruit developmental stages (cherelle, young pod and adult pod); disease severity was assessed in laboratory conditions, on detached, artificially inoculated fruits, while disease incidence was assessed in the field, under natural inoculum pressure. In both assessment fruits at the cherelle stage were the most susceptible whereas the young and adult fruits were equally susceptible. The vertical position of the fruits on the tree did not influence their susceptibility. Estimates of the fruit susceptibility and of the infectious potential were derived from the severity and incidence measurements, using a model assuming that the number of spores on a fruit follows a Poisson distribution with the mean, the density of spores per fruit as the parameter. The estimated parameter values allowed the evaluation of the probability of attack of a fruit by the disease, which could be implemented in a disease warning system.
KeywordsInfectious potential Model Poisson variable
IRAD (Institute of Agricultural Research for Development, Cameroon), INRA (National Institute of Agricultural Research, France), ENSP (National Polytechnic Institute, Cameroon) and SCAC (Service for Cooperation and Cultural Action, French Embassy in Cameroon) are acknowledged for the support provided. Sincere thanks are expressed to the staff of the Plant Pathology Laboratory of IRAD who carried out the field and laboratory trials, as well as to Mr. Awah N. Richard who critically reviewed the manuscript.
- Ando, K., Hammar, S., & Grumet, R. (2009). Age-related resistance of diverse cucurbit fruit to infection by Phytophthoracapsici. Journal of the American Society for Horticultural Science, 134, 176–182.Google Scholar
- Berry, D. (1999). Lutte chimique raisonnée et techniques culturales. In D. Mariau (Ed.), Les maladies des cultures pérennes et tropicales (pp. 282–294). Montpellier: Cirad.Google Scholar
- Cox, D. R. (1972). Regression models and life tables. Journal of the Royal Statistical Society, B, 34, 187–220.Google Scholar
- Dacunha-Castelle, D., & Duflo, M. (1992). Probabilités et Statistiques: problèmes à temps fixe. Paris: Masson.Google Scholar
- Deberdt, P., Mfegue, C. V., Tondje, P. R., Bon, M. C., Ducamp, M., & Hurard, C. (2007). Impact of environmental factors, chemical fungicide and biological control on cocoa pod production dynamics and black pod disease (Phytophthoramegakarya) in Cameroon. Biological Control, 44, 149–159.CrossRefGoogle Scholar
- Ducamp, M., Nyasse, S., Grivet, L., Thévenin, J. M., Blaha, G., Despréaux, D., & Cilas, C. (2004). Genetic diversity of cocoa tree Phytophthora pathogens. In C. Cilas & D. Despréaux (Eds.), Improvement of cocoa tree resistance to Phytophthora disease (pp. 45–75). Montpellier: CIRAD.Google Scholar
- Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. London: Chapman and Hall.Google Scholar
- Monfort, A. (1971). Cours de statistique mathématique. Paris: Economica.Google Scholar
- Ndoumbe-Nkeng, M. (2002). Incidence des facteurs agro-écologiques sur l’épidémiologie de la pourriture brune des fruits du cacaoyer au Cameroun: contribution à la mise en place d’un modèle d’avertissement agricole. Paris, France: INA-PG, PhD thesis.Google Scholar
- R Development Core Team. (2008). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
- Soubeyrand, S., Sache, I., Lannou, C., & Chadœuf, J. (2007). A frailty model to assess plant disease spread from individual count data. Journal of Data Science, 5, 67–83.Google Scholar