Mr. Laubscher-Pretorius’s research applies machine learning approaches to optimise Eucalyptus species recommendations across environmental gradients in KwaZulu-Natal and Mpumalanga, South Africa. His work focuses on improving genotype-site matching by integrating environmental variables, including temperature, precipitation, and site-specific conditions, with historical tree performance data.

Through predictive modelling, the project aims to identify the key drivers of Eucalyptus growth across diverse plantation landscapes and support more informed deployment strategies. The research will contribute to the development of a decision-support tool for forest managers, promoting more sustainable, data-driven, and efficient plantation management practices.


