Coffee tree architecture and its interactions with microclimates drive the dynamics of coffee berry disease in coffee trees

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.

Alternative plant protection strategies for tomorrow's coffee

“…Shade trees provide a physical barrier for coffee against air dispersal of pathogens (Bedimo et al, 2012; Soto‐Pinto et al, 2002), but may enhance the overwintering of some insects or fungi through microclimate modification, which favours specific developmental stages in infestation or disease onset (Righi et al, 2013). In particular, each of the foliar fungal pathogens reviewed here were found to increase in intensity under shaded systems when conditions favoured life cycle events, for example, H. vastatrix (Avelino et al, 2020; Gagliardi et al, 2021), C. kahawae (Motisi et al, 2019) and M. citricolor (Avelino et al, 2007; Johnson et al, 2009). These dynamics must be considered on a case‐by‐case manner by the coffee farmer when implementing or establishing a coffee‐AFS.…”

“…Coffee berry disease (CBD) is caused by the ascomycete fungus C. kahawae and is currently confined to the African continent (Motisi et al, 2019; Nonoh, 2007; Nonoh et al, 2010). Severe disease incidence can lead to up to 90% yield losses due to the pathogen targeting the coffee berry.…”

“…This may be due to its geographic limitation to Latin America or the fact that it is not as frequently prevalent compared to other foliar leaf fungal pathogens (i.e., CLR). kahawae and is currently confined to the African continent (Motisi et al, 2019;Nonoh, 2007;Nonoh et al, 2010). Severe disease incidence can lead to up to 90% yield losses due to the pathogen targeting the coffee berry.…”

Long-term effects of shade and input levels on coffee yields in the Pacific region of Nicaragua

“…The shaded coffee system used more water and at a greater rate at the beginning of the two dry seasons evaluated, explained by a better exploration of the roots of shade trees in the soil profile. Yet, when the shaded coffee system faced a severe dry season the advantage of better exploration was inexistent as it seems that underground water from the systems was no longer available for the crops and the shade trees (Motisi et al, 2019).…”

Tree architecture and functioning facing multispecies environments: We have gone only halfway in fruit‐trees

“…Those approaches are especially relevant to fruit trees in multispecies agroecosystems where shade is a main driver of plant growth and functioning with strong effects on resource partitioning among the various tree components, such as branch length and distribution, and relationships between vegetative growth and flowering. Such work has the potential to better predict fruit production (e.g., apple; Pitchers et al, 2021) or the dynamics of disease (e.g., coffee; Motisi et al, 2019).…”