Local AI trained on gut microbe data could help African livestock withstand heat
In a study published in Agriculture, researchers from the University of Johannesburg (UJ) propose a policy roadmap for an AI-driven precision farming approach to further improve indigenous and other livestock breeds' abilities to withstand extreme conditions in farming operations of all sizes and types.
A new study from the University of Johannesburg proposes an AI-driven precision farming approach to enhance the resilience of indigenous African livestock to extreme conditions. As the continent anticipates one of the strongest El Niño events ever observed, maintaining the production of eggs, milk, and meat from stressed animals will become increasingly challenging.
However, smallholder and small-scale farmers face significant barriers in accessing high-nutrition feeds, imported antibiotics, and other veterinary drugs. Indigenous African livestock breeds, which are adapted to local conditions, are more resilient and require fewer medications. These breeds can survive heat and drought that are lethal to other breeds, but they gain weight more slowly.
Researchers argue that employing local AI data models built on data from typical African farms could improve production and reduce disease in these animals. Such models would consider the specific needs of indigenous breeds and the unique challenges faced by smallholder farmers.
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