Coffee berry disease (CBD) can cause significant coffee yield losses along with major income losses for African smallholders. Although these farmers cannot afford to purchase pesticides to control the disease, agroecological solutions have rarely been investigated, and how epidemiological mechanisms are linked to the environment of the coffee tree and the plot remains unclear. Agroforestry systems are a promising agroecological option, but the effect of shade on CBD regulation is the subject of debate, and the use of plant species diversity remains uncertain. Here, we address how shade affects epidemiological mechanisms by modifying the microclimate. For this purpose, we developed a mechanistic susceptible-exposed-infectious-removed model and used a Bayesian framework to infer the epidemiological parameters against microclimatic covariates. We show that shade has opposing effects on different epidemiological mechanisms. Specifically, shade can limit disease dynamics by reducing disease transmission while simultaneously promoting disease dynamics by reducing the latent period of the pathogen. However, in full sun, efficient disease transmission compensates for long latent periods. As a result, the balances between microclimatic variables can counterbalance the epidemiological rates, which can dramatically alter the fate of epidemics in shade versus full sun conditions. We propose research avenues to help design cost- and environmentally effective management strategies for CBD that are notably based on the functional traits of shade trees that could hamper CBD dispersal.
Keywords: Bayesian inference; Coffea arabica; Colletotrichum kahawae; agroforestry systems; mechanistic-statistical approach; susceptible-exposed-infectious-removed (SEIR) model.