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Machine Learning and Decision Modeling for Climate-Smart Beef Production in South Africa

PI and co-PIs: Karun Kaniyamattam (Texas A&M University, United States); Abubecker Hassen (University of Pretoria, South Africa); Serinmary Pulikkottil Rejimon (Texas A&M University, United States); Abuye Tulu Demisse (University of Pretoria, South Africa); Ziyanda Goli (University of Pretoria, South Africa); Demian Johnson (University of Pretoria, South Africa); Georgette M. Pooys (ARC-Irene, Animal Production Institute, South Africa); Michael Scholtz (ARC-Irene, Animal Production Institute, South Africa)

Funding amount: $150,000

Project overview: South Africa is home to approximately 13 million beef cattle, representing a major source of greenhouse gas emissions. However, coordinated data to shape emissions-reduction plans is lacking. Working in South Africa’s diverse rangelands, this project will combine datasets on ecosystems, animal nutrition, and agricultural performance, using machine learning to predict emissions-reduction measures. The team will deliver an interactive dashboard that empowers farmers and policymakers to adopt climate-smart practices, improving efficiency and resilience. By creating open datasets and scalable AI models, this work will not only transform beef production in South Africa but also serve as a blueprint for sustainable livestock systems worldwide—advancing global efforts to curb methane and fight climate change.

Agriculture & Food Computer Vision & Remote Sensing