Professor and Australian Research Council Laureate Fellow 
The University of Sydney, Australia 

Prof Alex McBratney is a globally recognized soil scientist whose research has helped shape the modern fields of pedometrics, digital soil mapping, precision agriculture and soil security. His pioneering work has transformed how soils are measured, mapped and managed, advancing sustainable land use, agricultural productivity, and climate-smart agriculture. 

His research spans soil variability, digital soil information systems, and soil sensing technologies, with a lasting influence on global soil science and environmental stewardship. 

Alex is a Fellow of the Royal Society and the Australian Academy of Science. He is a former Deputy Secretary General of the International Union of Soil Sciences and recipient of the VV Dokuchaev Medal, soil science's highest honor. 

Professor of Soil-Landscape Modelling 
The University of Sydney, Australia 

Prof Budiman Minasny is a leading soil scientist whose research has advanced digital soil mapping and modelling to improve the measurement, monitoring and management of soil carbon worldwide. He has used breakthrough soil mapping and modelling techniques to unlock new knowledge on soil carbon stocks and developed cost-effective techniques to rapidly map soil properties across scales, from individual paddocks to the global level. 

His research underpins applications in soil carbon benchmarking, sustainable agriculture, ecosystem management and climate change mitigation. He is particularly passionate about the role of soils in supporting food, water and energy security while maintaining biodiversity. 

Budiman is a Fellow of the Australian Academy of Science, and his work has influenced soil science research and policy worldwide.

Soil Scientist - Digital Soil Mapping 
NEIKER BRTA, Spain 

Dr Mercedes Román Dobarco is a soil scientist whose research explores the processes that control soil organic carbon storage and stability, and how this knowledge can be applied to address climate change, food security and sustainable land management. She combines soil ecology, spectroscopy and digital soil mapping to better understand and predict soil properties across landscapes. 

Her work has advanced methods for mapping soil carbon stocks, soil biodiversity and soil change, while developing innovative tools that support soil monitoring and soil security. Her research has contributed to large-scale soil information products used in agricultural management, land-use planning and carbon accounting. 

Her work bridges fundamental soil science and practical applications, helping translate complex soil processes into actionable knowledge for environmental decision-making.

Director, CFAES Rattan Lal Center for Carbon Management and Sequestration and Professor of Soil Science 
The Ohio State University, USA 

Prof Rattan Lal is a world-renowned soil scientist whose research has transformed understanding of the role of soils in addressing climate change, food security and environmental sustainability. He is particularly known for pioneering work on soil carbon sequestration and demonstrating how restoring soil health can improve agricultural productivity while mitigating greenhouse gas emissions.  

By highlighting the critical role of soil carbon in regulating the Earth's climate, his research has influenced global climate policy, sustainable agriculture initiatives, and natural resource management worldwide. 

Rattan was awarded the prestigious World Food Prize in 2020 and is also a co-recipient of the 2007 Nobel Peace Prize through his significant contributions to the Intergovernmental Panel on Climate Change (IPCC) reports. Committed to education, Rattan has mentored 430 researchers. 

Assistant Professor for Geo-Environmental Data Science 
Utrecht University, the Netherlands 

Dr Madlene Nussbaum is a computational geographer and environmental data scientist whose research focuses on developing statistical and machine learning approaches for understanding and mapping complex environmental systems. She is particularly interested in combining spatial data, predictive modelling, and sampling design to improve the accuracy and reliability of environmental information. 

By adapting statistical and machine learning techniques to the challenges of soil and spatial data, she helps generate robust information for environmental monitoring, land management, and decision-making. 

Madlene’s research bridges data science and environmental applications, with a strong emphasis on reproducible analysis, uncertainty assessment, and the effective communication of spatial information.