Dr Alexandre Wadoux

Alexandre Wadoux is a Research Associate in soil science at the University of Sydney, Australia. He has an undergraduate degree from the University of Angers in France, a MSc in soil science from the University of Tubingen in Germany, a Master in epistemology of sciences from the University of Nantes in France and a PhD in applied geostatistics from Wageningen University in the Netherlands. Dr Wadoux’s research has been on understanding how soil develops and varies in space and time. He created and developed new methods for soil sampling and quantitative soil mapping, particularly using kriging-based techniques. He also developed techniques for uncertainty quantification of digital soil maps generated by deep learning algorithms. More recently, his interests moved towards better understanding on the use of statistical learning algorithms in soil science: how to interpret machine learning models and how to extract pedological knowledge from them. He is also part of the Australian Terrestrial Ecosystem Research Network in collaboration with the CSIRO to map soil biodiversity and soil carbon dynamic at high spatial resolution in Australia.
Email: alexandre.wadoux@sydney.edu.au

Prof. Dr. Alessandro Samuel-Rosa

I have been a professor and researcher at the Federal Technological University of Paraná, Campus Santa Helena (UTFPR-SH), since March 2018. After my first studies at the Federal University of Santa Maria (UFSM) on the complex interactions between land use and Soil quality, in 2012, I moved to the field of quantitative pedology, better known as pedometrics. Since then, the collaboration with researchers from the National Soil Research Center (Embrapa Solos), the International Soil Reference and Information Center (ISRIC), the Wageningen University and Research (WUR), and the Federal Rural University of Rio de Janeiro (UFRRJ ), allowed me to become familiar with multiple pedometric methods and techniques. Today my main academic-scientific interest is pedometrics, focusing on the study of spatial sampling strategies, the selection and calibration of (geo)statistical models, the analysis of error propagation, and the management of open data. I have recently published some relevant articles on these topics in national and international magazines. I am also the author of three packages for R – pedometrics, spsann, and febr – and maintainer of the Free Brazilian Repository for Open Soil Data (febr).

Professor Dr. Budiman Minasny

Speech regarding “Modelling framework for national class mapping” and probably a shortcourse on “Spectroscopy with R”. Professor in Soil-Landscape Modelling (School of Life and Environmental Sciences).

Biographical details
Budiman Minasny is a Professor in soil-landscape modelling at the University of Sydney. He was awarded various prestigious fellowships including the QEII and the Future Fellowships from the Australian Research Council. He has an undergraduate degree from Universitas Sumatera Utara in Indonesia and a MAgr and PhD degrees in soil science from the University of Sydney. He is passionate about the role of soil in managing climate change, food, water, energy security and maintaining biodiversity. He has more than 150 international journal publications, won numerous awards, and is recognised as the leader in digital soil mapping and modelling. He is also a member of the Sydney South East Asia Centre and China Studies Centre.
Email: budiman.minasny@sydney.edu.au

Prof. Dr. Elpidio Inacio Fernandes Filho

Graduated in agronomy from the Federal University of Viçosa(1986), master’s degree in Agronomy (Soils and Plant Nutrition) from the Federal University of Viçosa(1989) and Ph.D. in Agronomy (Soils and Plant Nutrition) from the Federal University of Viçosa(1996) . He is currently a Professor at the Federal University of Viçosa. He has experience in the field of Agronomy, with an emphasis on Pedometrics. Acting mainly on the following topics: Specialist systems, Agricultural fitness, Geographical information systems, software development.

Prof. Dr. José Alexandre M. Demattê

Pedologist, Professor of Remote Sensing Applied to Soils at the University of São Paulo, ESALQ-USP, Department of Soil Science. Named one of the 100,000 most cited researchers in the world. Coordinates the United Nations Premium Level Spectroscopy Laboratory, the Group of Geotechnologies in Soil Science, National and International Course (ProBASE), the Brazilian Soil Spectral Library and participates in the World Soils (European Space Agency), Pronasolos and Map Biomes.

Dr. PhD Laura Poggio - ISRIC

Laura has a background in forestry and environmental sciences and a PhD in Soil Science. Her main research interest is in Digital Soil Mapping and how to integrate soil data in the wider environmental/earth modelling. The main topic is the development of spatial, (geo)statistical and Machine Learning models to integrate ground observations with remote sensing data at different spatial scales to assess the role of soil resources within wider ecosystem services, to support sustainable development and conservation goals.

Prof. Dr. Marcos Bacis Ceddia

Graduated in Agronomy Soil Science from the Federal Rural University of Rio de Janeiro (1992), Master in Agronomy (Soil Science) from the Federal Rural University of Rio de Janeiro (1996) and Ph.D. in Agronomy (Soil Science) from the Federal University of Rio de Janeiro (2000). He is currently a professor at the Federal Rural University of Rio de Janeiro and a researcher at CNPq (Productivity -PQ2). He has experience in the field of Agronomy, with an emphasis on Digital Soil Mapping, working mainly on the following topics: soil physics, geostatistics, agroecology, digital mapping and geoprocessing. He is a permanent professor at the Postgraduate Program in Agronomy Soil Science (PPGA-CS / UFRRJ) and at the Postgraduate Program in Computational Mathematical Modeling (PPGMMC / UFRRJ). He is currently coordinating the following projects: 1- Digital Soil Mapping of Oil and Gas Production Areas: Case study of the Central and Northeastern Brazilian Amazon Fields; 2- MultSoils: An e-Science Platform for Soil Governance and Soil Safety; 3- Spatial analysis of soil moisture in an organic production system, using wireless sensor networks; 4- Museum of Soils of Brazil/UFRRJ

Dra. Maria de Lourdes M Santos Brefin

Graduated in Agronomy from the State University of Maranhão (1986), Master in Agronomy (Soil Science) from the Federal Rural University of Rio de Janeiro (1990), Master in Environmental Science (Troisième Cycle) from “École Polytechnique Fédérale de Lausanne”, Switzerland (1995) and Doctorate in Sciences, Pedology and Geomatics (Doctorat ès Sciences en Pédologie et Géomatique) from the “École Polytechnique Fédérale de Lausanne”, Switzerland (1999). He holds a Post-Doc from The Sydney University, Australia in Digital Soil Mapping. She is researcher A at Embrapa Solos. She served two terms as head general, from 2009 to 2011 and from 2011 to 2014. She develops research in the area of ​​Soils in interface with Geomatics, especially in the areas of Quantitative Pedology and Soil-Landscape Modeling through Digital Soil Mapping (MDS ). She participates in postgraduate courses at several universities, as a speaker, on thesis defense boards and in the supervision of undergraduate and graduate work. She is a member of the Brazilian Society of Soil Science and a member of its Editorial Committee. Today, after selection, she holds the position of General Head of Embrapa solos.

Dr. Silvio Barge Bhering

Graduated in Agricultural Engineering from the Federal Rural University of Rio de Janeiro (1983), with a concentration in Soil Science, has a postgraduate degree in Systems Analysis and Design from PUC-RJ, an MBA in Business Management (UFSC) and an MBA in Management of Projects (USP/Esalq). He holds a Masters in Engineering in the Geoprocessing concentration area from the Polytechnic School of the University of São Paulo (1995) and a PhD in Geography in the area of Environmental Planning and Management from the Federal University of Rio de Janeiro (2007). He acts as coordinator of Research Projects in the themes of Land Use and Occupation Planning, Environmental Modeling and Digital Soil Mapping. He currently coordinates the Soil Research Project Portfolio in Brazil in the Embrapa System.

Professor Dr. Tomislav Hengl

Tom is the Co-founder of OpenGeoHub foundation. He is directly responsible for the Summer school courses, which since 2007 have provided leading edge training, directed primarily at postgraduate students and early career scientists, in the use of Open Source Software tools (R / OSGeo) for spatial statistics and spatial analysis.

Email: tom.hengl@opengeohub.org

Dr. Philippe Lagacherie

Philippe Lagacherie trained as an Agronomist and completed his PhD in soil science in 1992. He obtained his senior scientist degree from Montpellier 2 University in 2002 for his researches on digital soil mapping methods. He is currently senior researcher at INRAE Montpellier (France) and has led from 2016 the French network of research on Digital Soil Mapping. Dr Philippe Lagacherie has been involved in research dealing with Digital Soil Mapping since the eighties, and has been working also in remote sensing and spatial modelling of cultivated landscapes. In Digital Soil Mapping, he organised in Montpellier, the first Global Workshop on Digital Soil Mapping and developed spatial approaches that associated field knowledge, GIS, remote sensing, geostatistics, and fuzzy logic. He has been involved in the GlobalSoilMap initiative since 2007. He authored more than 110 papers and chapters in international journals and books.

Dr. Phillip R. Owens

Dr. Phillip R. Owens is the Research Leader at the USDA, Agricultural Research Service (USDA-ARS) Dale Bumpers Small Farms Research Center in Booneville, AR. After receiving his B.S. and M.S. at the University of Arkansas, he completed a Ph.D. in soil science from Texas A & M University in 2001. He worked in the office of U.S. Senator Blanche Lincoln as a Congressional Science Fellow and then as Research Soil Scientist with USDA-ARS at Mississippi State University. Dr. Owens was an Associate Professor of Pedology/Soil Geomorphology in the Department of Agronomy at Purdue University and spent 1 year on sabbatical at CIAT in Cali Colombia. While on faculty he developed 2 patents using remotely sensed elevation data and machine learning, which are utilized by 2 agricultural companies focused on precision soil management. Dr. Owens’ current research interests include soil and landscape processes related to soil health and agricultural sustainability. He continues to develop tools incorporating proximal and remote sensing technology for applications for small holder farmers.

Dr. Owens focuses on small farms around the world and particularly in Central America where he led the Water Smart Agriculture project’s digital mapping program in El Salvador, Honduras, Nicaragua and Guatemala. He is the past Chair of the Pedology Division of the Soil Science Society of America, past Associate Editor of Soil Science Society of America Journal, Co-Chair of New Technologies in Soil Survey Committee, Facilitator of the IUSS Universal Soil Classification System Committee, and former member of the National Soil Survey Advisory Board.

Dra. Wartini Ng

An early career scientist recognized for solid verbal and written communication skills. An enthusiastic, and fast-learning person with huge interest in data exploration. I look forward to digging into complex data and produce data-driven recommendations. Skilled in programming, machine learning, deep learning, statistics, and problem solving.
PhD in Faculty of Science at The University of Sydney. Dec 2019.
Dissertation: Contemporary data analytics for soil spectroscopy.
Research Assistant Sept 2019 to current.
Developed calibration functions for near infrared spectrometer for the Indonesian Center for Agricultural Land Resources Research (ICALRD); collaborated with USDA-NRCS researchers in developing spectral transfer calibration library; identified the decomposition and stabilization of litter using Australian tea brands.
Developed new predictive models for spectroscopy data, and analyzed the performance of machine learning against deep learning (convolutional neural network) trained using ~20k samples national soil samples along with its spectral data