Chocolate, a cherished delight worldwide, faces a significant peril due to a swiftly spreading virus targeting cacao trees, the source of its key ingredient. This jeopardizes the global chocolate supply. Nonetheless, a group of scientists is combating this threat using an unconventional tool: mathematics.
Cacao swollen shoot virus disease ravaging Cocoa trees
Cacao swollen shoot virus disease (CSSVD) is a destructive pathogen causing significant harvest losses in cacao trees, especially in West Africa, a major source of global chocolate production. Spread by mealybugs, it devastates trees by feeding on their leaves, buds, and flowers. Ghana has lost over 254 million cacao trees to this virus in recent years.
According to a recent study published in PLoS ONE and co-authored by Benito Chen-Charpentier, a professor of mathematics at The University of Texas at Arlington, the virus poses a significant threat to the global chocolate supply. Traditional methods like pesticides have proven ineffective against the virus-carrying mealybugs, leading to significant losses in Ghana, where over 254 million cacao trees have been lost in recent year
In a world without chocolate, familiar comforts like hot cocoa and delightful treats like chocolate bars would vanish. If CSSVD keeps spreading, this bleak future could become a reality.
Scientist leverage mathematical data to protect cocoa trees
A potential solution for combating the threat to chocolate production posed by mealybugs is vaccinating cacao trees against the virus they carry. However, this approach presents challenges such as cost and reduced harvest yields.
To address these issues, Chen-Charpentier and collaborators from multiple institutions have devised a novel strategy. By utilizing mathematical data, they optimize the vaccination process, enabling farmers to protect their crops while mitigating expenses and harvest losses. This approach hinges on comprehending the transmission dynamics of the virus by mealybugs.
The research team developed mathematical models to protect cacao trees from disease, considering factors like infection delay and disease spread randomness. By validating these models with real-world data from Ghana, they improved their accuracy. These models aim to safeguard crops, benefiting farmers economically and supporting global chocolate demand.
