Abstract
Coffee berry disease (CBD), which is widespread in Africa, has been responsible for massive yield losses of Coffea arabica. In Cameroon, C. arabica is mainly planted in agroforestry systems on smallholder farms, where low incomes hinder the use of chemicals to manage the disease. Novel agroecological strategies for controlling CBD are expected to be implemented and even increase in the current context of global changes. In this study, we showed that coffee tree architecture and its interactions with microclimates were important to CBD cluster symptom appearance (CSA), with notable CSA increasing along the tree branch away from the trunk to the tip of the branch. As shade trees can modify microclimates, we further investigated scenarios of various microclimatic conditions under shade to explore the effects of agroforestry systems on CBD dynamics in coffee trees. We showed that shade could result in contrasting effects on disease dynamics, decreasing CSA along the branch and increasing epidemic duration. We suggest that the contrasting effects of shade on disease dynamics need further evaluation of the possible trade-offs among the variables at play, and we recommend a combination of epidemiological and architectural modelling to help design more cost-effective and environmentally friendly CBD management strategies.
Highlights
Coffee berry disease (CBD), caused by the fungal pathogen Colletotrichum kahawae, is widespread in Africa and, depending on climatic conditions, has been responsible for massive coffee yield losses, with up to 90% loss recorded[1]
We investigated whether coffee tree architecture can influence CBD cluster symptom appearance (CSA, Fig. 1c) and analysed tree architecture effects relative to those of microclimates
The CBD latent period can vary between one week and one month; both precipitation and suitable conditions for infection were considered within three time windows, namely, 7 to days, to days and to 29 days before CSA
Summary
Coffee berry disease (CBD), caused by the fungal pathogen Colletotrichum kahawae, is widespread in Africa and, depending on climatic conditions, has been responsible for massive coffee yield losses, with up to 90% loss recorded[1]. We applied a statistical method from the field of machine learning, boosted regression trees (BRT), which enables researchers to address questions relative to complex biological systems involving many variables and characterized by multiple interactions between processes We used this approach to assess the relationship between CSA and the previously mentioned explanatory variables by fitting a model to the field data containing disease dynamics on coffee trees cropped in the banana shading system. We subsequently studied whether agroforestry systems can modify disease dynamics by investigating in silico the hypothesis that the microclimate induced by forestry tree can decrease CBD levels For this purpose, we applied a new set of microclimatic variables (precipitation, temperature and relative humidity) measured over two years under a kola shade tree (Fig. 2b) present on the coffee farm, hereafter referred to as the kola shading system, to the previous fitted model used as the reference situation. We encourage the use of mechanistic modelling to help design novel, more cost-effective and environmentally friendly management strategies at both the tree scale and plot scale
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