Potato virus Y and Potato leafroll virus management under climate change in sub-Saharan Africa
Keywords:PVY, PLRV, seed certification, aphids, cultural control
Potato has increased in importance as a staple food in sub-Saharan Africa, where its production is faced with a multitude of challenges, including plant disease development and spread under changing climatic conditions. The economically most important plant viruses affecting potatoes globally are Potato virus Y (PVY) and Potato leafroll virus (PLRV). Disease management relies mostly on the use of insecticides, cultural control and seed certification schemes. A major obstacle in many sub-Saharan Africa countries is the availability of disease-free quality seed potatoes. Establishment and implementation of quality control through specialised seed production systems and certification schemes is critical to improve seed potato quality and reduce PVY and PLRV sources. Seed could be further improved by breeding virus-resistant varieties adapted to different environmental conditions combined with management measures tailored for smallholder or commercial farmers to specific agricultural requirements. Innovative technologies – including more sensitive testing, remote sensing, machine learning and predictive models – provide new tools for the management of PVY and PLRV, but require support for adoption and implementation in sub-Saharan Africa.
- Potato virus Y (PVY) and Potato leafroll virus (PLRV) are the two major potato viruses threatening profitable seed potato production.
- High-quality seed shortage in many sub-Saharan Africa countries has been identified as a constraint to increasing yield.
- Specialised seed grower or seed certification programmes should be implemented to prevent virus transmission from seed to daughter tubers.
- Sustainable PVY and PLRV management in seed potatoes requires specific regional approaches to growth, farming and climatic conditions.
- Future research should include predictive models and new innovative technologies such as more sensitive testing, machine learning and remote sensing.
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