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Occurrence and Distribution of Common Bacterial Blight and Bacterial Leaf Blight in Tanzanian Lowland and Highland Agro-ecologies using Digital Image Analysis

Mulungu, E. L.; Madege, R. R.; Mwaipopo, B.; Sanga, C.; Mahenge, M. J.; Ishengoma, F.

2025-12-13 plant biology
10.64898/2025.12.10.693561 bioRxiv
Show abstract

Common Bacterial Blight (CBB) and Bacterial Leaf Blight (BLB) are important constraints affecting paddy and bean yields, respectively, causing significant losses if unmanaged. For effective planning and management of such diseases, accurate and reliable quantification, i.e., prevalence, incidence, and severity, becomes crucial. Despite this, the quantification of these diseases in Tanzania remains limited. Here, digital image analysis (ImageJ and Plantix) was used in the identification and quantification of CBB and BLB in low and highland agroecologies represented by Kilosa and Mbarali districts, respectively, by surveying 24 paddy and common bean fields across 10 villages. The study revealed that CBB and BLB were highly prevalent (100%) across study sites, with varying incidence and severity. Incidence and severity of both CBB and BLB were significantly higher in Kilosa than in Mbarali, with a large proportion (>19%) of these variations accounted for by district-level differences. Interestingly, significant variations in incidence and severity were observed even at the village level. In Kilosa, CBB severity differed significantly among villages (p = 0.018), while in Mbarali, BLB incidence and severity varied significantly (p < 0.001; p = 0.0436). Village-level differences accounted for over 41% of the total variation. Conclusively, the study indicates that CBB and BLB are highly prevalent across both Kilosa and Mbarali districts and their respective villages, with incidence and severity varying due to both district- and village-level differences. To manage these diseases effectively, site-specific management and geographically targeted interventions are required that account for local variability while prioritizing areas of higher infection risk, such as Kilosa. This approach will not only improve plant disease management programs but also ensure efficient resource use, thereby promoting sustainable disease control under changing climatic conditions.

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