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Developing beneficial nematode-based biocontrol solutions to fall armyworm in Africa

The fall armyworm is a major pest devastating more than 80 crops. However, it favours maize where it can cut yields by up to 90%. The pest has invaded Africa, Australia and Asia, and recently arrived in Europe. Existing pest management efforts against the maize pest include insecticides. But an overreliance on these has led to prolific increases in insecticide applications in maize cultivation in Africa, and detrimental health and environmental threats. The ineffective existing control method has highlighted the need for more effective, safer and more sustainable control practices. An expert team and CABI are developing practical, safe and effective techniques of entomopathogenic nematodes as biological control agents against armyworm caterpillars to help mitigate the impacts of fall armyworm on food security in Africa.

Biocontrol for papaya mealybug: lessons learnt from Kenya

Smallholder farmers’ knowledge, attitudes and practices towards biological control of papaya mealybug in Kenya

Farmer perceptions are highly important in influencing on-farm pest management…

Assessment of the socio-economic impacts associated with the arrival of apple snail (Pomacea canaliculata) in Mwea irrigation scheme, Kenya

In Kenya, rice (Oryza sativa L.) is mainly produced under irrigation…

Insights into farmer group effectiveness for promoting the adoption of safe food production standards

Earth observation to improve critical datasets for pest risk modelling

Rising temperatures have led to pests, diseases and weeds establishing in areas of the world that were previously uninhabitable. Furthermore, growth in global trade and new trade pathways increase the risk of accidental movement of pests. Earth Observation (EO) and climatic data can help by improving predictions about where potential agricultural pests and diseases may be a threat. Information produced by models can help decision makers understand and prepare for future risks. Working with a consortium of researchers, this project will use EO data to improve the data layers used in models that predict where pests can establish, including irrigation, areas under protected agriculture and climatic canopy conditions, demonstrating the improvements made to species distribution estimations for key pests and biological control agents.