Modeling the temporal evolution of cocoa black pod rot disease caused by Phytophthora megakarya

Abstract

Black pod rot, caused by Phytophthora megakarya, is the main cause of cocoa losses in Cameroon. A few studies have focused on describing black pod epidemics in cocoa yet numerous questions remain. Here, an epidemiological model describing the temporal evolution of cocoa black pod, taking into account the development stages of pods, is developed and studied. In particular, the relative importance of primary and secondary infection in disease dynamics is investigated. Our theoretical study shows the existence of a disease free equilibrium and at least one endemic equilibrium. We highlight two threshold parameters, related to direct and indirect infections that summarize all possible dynamics of the system. Then, based on the literature, we define a periodic pod recruitment function and provide several numerical simulations to study the impact of phytosanitary pod removal on disease dynamics. We show that intense and regular sanitary harvest could lead to complete disease eradication.Our results also highlight the importance of the environmental spores reservoir in disease dynamics, such that future field experiments and observations should focus on it. KEYWORDS analysis, black pod rot, cocoa, disease, epidemiological model, simulations 8816

Spatial and Temporal Analysis ofPhytophthora megakaryaEpidemic in Newly Established Cacao Plantations

“…The results also indicate that primary inoculum does not only initiate the epidemic but continues to induce infections throughout the year(s). The exact Fisher test performed for individual plots (data not shown) and the combined data from all plots confirmed this hypothesis and these results are in line with Nembot et al (2018) who also demonstrate the importance of primary inoculum in P. megakarya disease epidemics. Moreover, in all plots and throughout the years, primary infections occur at distances of over 6 meters from any given potential inoculum source.…”

“…PSA are governed by 1) dispersal and/or 2) the environment (Koenig, 1999). Given the short natural dispersal distance mechanism due to rain-splashing of P. megakarya (Gregory & Madison, 1981), and the weekly removal of infected pods, which limits secondary infections (Nembot et al, 2018) this explains the small core cluster size. These results do indicate however, given the aggregated patterns and PSA, that disease spread is step by step to neighbours of infected trees.…”

“…Yet, in well-established infected cacao plantations, phytosanitation and fungicide spraying do not normally cause such a drastic reduction in black pod rot as observed here. Weekly phytosanitation is insufficient to eliminate secondary inoculum production completely (Ndoumbé Nkeng et al 2004;Nembot et al, 2018;Opoku et al 2007). Ndoumbé Nkeng et al (2004) even found higher pod rot levels after rigorous phytosanitation efforts the year before.…”

Exploring the Impact of Climatic Variables on Arecanut Fruit Rot Epidemic by Understanding the Disease Dynamics in Relation to Space and Time

“…These results were consistent with the reports demonstrated the persistent dew on the pod surface for up to 3 h after sunrise is ideal for black pod rot disease development [ 52 ]. Primary inoculum initiates disease and influences the development of epidemic thought-out the year was demonstrated on cocoa plantations [ 53 ].…”

Assessment of the Spatial Distribution and Risk Associated with Fruit Rot Disease in Areca catechu L.

“…Understanding the spatial distribution of FRD can help in the determination of inter-relationships between inoculum build-up and disease severity, the influence of abiotic factors on pathogen dynamics, and in risk assessment of disease in plants other than arecanut [34][35][36][37][38]. Different spatial disease distribution analysis methods are used to characterize the spatial positions of pathogens and locations of disease-affected gardens [39][40][41][42][43]. Spatial autocorrelation is generally used to determine the correlation between spatial data at different distance intervals in order to derive spatial dependence [42,44].…”