Diagnosis and mapping of African plum ( Dacryodes edulis [(G Don) H.J.Lam] diseases in three agro-ecological zones of Cameroon

Abstract

African plum is an important fruit tree native to the Gulf of Guinea. Its fruits are consumed and sold in urban markets in African and European markets, generating substantial income for producers and traders. Diseases are the main biotic production constraint in Cameroon. However, diseases of the African plum in the orchards and during harvest are not yet studied in the country. This study aimed to identify African plum diseases in Cameroon agro ecological zones to reduce yield losses. Disease diagnosis was carried out in the field by visual observations of signs and symptoms and description of the general environmental conditions that could favour disease appearance and in the laboratory for pathogen isolation and pathogenicity confirmation. For each disease, its distribution was carried out using an analytical geographical approach. In the orchards, African plum is associated with trees and fruit diseases such as: Anthracnosis, Cercospora leaf spot, die-back, red rust, peri-peduncular fruit rot and leaf chlorosis. Cercospora leaf spot was significantly (p<0.005) the most severe disease (64.03%) in the Western highland and Bimodal rainfall forest agro-ecological zones, while anthracnosis was the most severe (50.46%) in the Monomodal rainfall forest zone. However, diseases diagnosis show that anthracnosis, Cercospora leaf spot and leaf chlorosis were more widespread in the Western highland agro-ecological zone (disease density = 49 to 61, 135 to 169 and 79 to 122, respectively) while red rust was mostly spread in the Bimodal and Monomodal rainfall forest zones (disease density = 71 to 89 and 53 to 71, respectively). Knowledge of diseases affecting African plum trees in different agro-ecological zones in Cameroon is essential for improving management and increasing yields. The diagnosis highlighted the presence of several diseases in the African plum orchards. The map representation confirms and visualizes the spatial distribution, showing the disease densities across the agro-ecological zones.