ArticleJournal of ethnobiology and ethnomedicine2026
Farmers' indigenous knowledge in the management of coffee berry disease caused by Colletotrichum kahawae and its status assessment.
Article in Journal of ethnobiology and ethnomedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCoffee berry disease (CBD), caused by Colletotrichum kahawae, is a major constraint to Coffea arabica production in Ethiopia, where coffee is crucial for livelihoods and national income. However, farmers' indigenous knowledge and local disease management practices remain underexplored. This study aimed to document farmers' knowledge of CBD detection and control and to assess disease incidence and severity across forest and semi-forest coffee systems along altitudinal gradients.
methodsA mixed-methods design was used, including structured household surveys of 90 experienced farmers across nine sites, focus group discussions (FGDs), key informant interviews (KIIs), and field assessments using berry count and visual scoring. Qualitative data were analysed thematically, while quantitative data on CBD incidence and severity were analysed using one-way ANOVA with Tukey's HSD test (p < 0.05) in R (v4.5.3). Boxplots were used to compare variation across systems.
resultsNearly all farmers accurately identified early CBD symptoms through visual observation and relied heavily on indigenous knowledge for management. Common practices included pruning, sanitation (removal of infected parts), and selection of locally preferred coffee types. Farmers also reported selecting genotypes perceived as resistant based on berry traits, indicating informal farmer-led selection. However, these practices showed limited effectiveness in controlling disease spread. FGDs identified climate variability, dense canopy management, and weak institutional support as key drivers of CBD intensification. KIIs revealed gaps in access to resistant varieties, weak extension services, and limited integration of farmer selection criteria into formal breeding. Significant differences in CBD incidence and severity were observed across sites and production systems (p < 0.05), with forest systems showing higher severity and variability than semi-forest systems.
conclusionsIndigenous knowledge plays a vital role in early CBD detection and local adaptation strategies. However, current farmer practices alone are insufficient for effective control. Integrated approaches combining local knowledge, resistant varieties, improved agronomic practices, and strengthened extension services are essential for sustainable CBD management and conservation of Ethiopia's Arabica coffee genetic resources.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.