ProfessorBudiman Minasny

Project Opportunities

Title: Digital Soil Mapping for the Countries in the Oceania Region

Summary of opportunity:

A Digital World Soil Properties Map consortium is formed (www.globalsoilmap.net). The objective of this consortium is to create a digital map of the world’s soil properties. At present, there is no comprehensive digital soil map of the world. This lack of easy-to-use information results in adverse policies on land use at the district, national, and continent levels in many developing and indeed developed countries that affect  food production for the poor, and cause unnecessary land degradation and increased greenhouse gas emissions. The University of Sydney is the training centre for the Oceania node. Potential students from Indonesia, the Philippines, Malaysia, Timor Leste, PNG, New Zealand, and the Pacific Island Nations are welcome to apply.

Opportunity synopsis:

Traditional soil mapping has been difficult and time-consuming as soils vary greatly in space and time. In addition, much soil information is lost because they are mapped in polygons reflecting soil types or classes. Within the last decade, there has been considerable progress in earth observation techniques and prediction of soil properties from data generated by remote and on-the-ground sensors. At the same time, mathematical and statistical techniques have been developed that allow for prediction of soil properties in areas with little or no information, as well as indicating the uncertainty of the predictions. The digital revolution that has transformed many of the sciences, and has created a new discipline called digital soil mapping.
The project will use the latest technology to create global soil maps. The project will provide training on the latest digital soil mapping methodology as a part of the GlobalSoilMap consortium activities. This project will be in collaboration with CSIRO Land and Water targetting staff and students from soil mapping agencies from countries in the Oceania node (Malaysia, Philippines, Indonesia, Singapore, Brunei, Timor Leste, Papua New Guinea, New Zealand, and the Pacific Island nations).

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Project Opportunities

Title: Soil-landscape dynamic modelling

Summary of opportunity:

Soil is the largest terrestrial store of carbon. Understanding causes and controls of spatial and temporal variation of this carbon pool is crucial for managing climate change, food water and energy security and for maintaining biodiversity.  Knowledge of how the spatial variability of soil C behaves dynamically is the key in understanding the impact of climate change and land management.  This project will combine the empirical data with process models in an optimal way to give a better prediction and understanding of the C distribution along with its confidence in prediction.

Opportunity synopsis:

There is a global demand for soil data and information for food security and global environmental management. As a consequence, there is now a large and growing interest in knowing the size of soil carbon pool and its sequestration potential . Digital soil mapping technology has progressed rapidly in the past decade, making it operational for routine mapping over large areas. While this empirical model has been successfully used to predict the spatial distribution of soil, the relationships are non-transferrable and no mechanism is involved. Furthermore, the dynamics of soil carbon with time means that we need to consider the time dimension in our maps. Meanwhile mechanistic or process-based models of soil C have been used effectively to predict the state of change in soil C. However, most C models are just an austere representation of a topsoil layer with no spatial component; it needs calibration, and the state (initial condition) of the soil need to be known first. We need to reverse the role, to use our process-based understanding in a mechanistic model to help us make a better prediction of the spatial distribution of soil properties, including carbon. This project will build a novel soil-landscape model which will predict the 4-dimensional space-time distribution of soil carbon. This innovative dynamic model will combine a process-based model with an empirical spatial data model under a Bayesian approach.  This project involves a considerable amount of modelling work, thus advanced skills in computer modelling is required. The student will acquire valuable knowledge and skills in the areas of soil carbon modelling. The student will also be expected to further develop skills in modern statistical and spatial data analysis and scientific publication.

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Project Opportunities

Title: Online digital mapping for better farm management

Summary of opportunity:

Digital agriculture draws decision to support on-farm management from a suite of soil and environmental data. Soil and many environmental data in Australia are currently available at a resolution of 100 m or coarser and not suitable for farm management and decision-making. This project will develop an online mapping tool which takes current field observations and maps and turns them into digital soil and climate maps at a fine resolution (30 m or finer) required for decision making. The methodology behind this tool is based on digital soil mapping technology developed in Sydney. The project will also develop efficient techniques to disaggregate coarser maps into fine resolution information. These maps will be combined with historical crop yield, weather data, crop management, and environment information to derive agronomic decisions for a farm meet its productivity and sustainability challenges. 
A complimentary scholarship for this project is available. To find out more, refer to the Postgraduate Research Stipend and Supplementary Scholarship in Digital Agriculture Data61. 
The supervisory team for this project also includes Dr Uta Stockmann (CSIRO), Dr Brendan Malone (CSIRO), Dr Ross Searle (CSIRO) and Dr Ross Darnell (Data61).

Opportunity synopsis:

Most big data in agriculture studies now show that soil variables are the most important factor predicting crop yield and performance. The project will develop new digital technologies that can provide on-farm soil information needed to better soil management which leads to increased crop productivity. 
 The project will fit with the upcoming TERN soil projects on delivering dynamic soil properties and could lead to a new project further strengthening existing collaboration with CSIRO and Data61.

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Project Opportunities

Title: Forecasting soil conditions

Summary of opportunity:

Not knowing where and how soil responds to climate change and human intervention compromises food, water, climate and energy security. Currently there is a lack of soil process knowledge and data infrastructure collectively causing significant uncertainty and risk in the assessments of key threats to soil. 

The project devises a new digital soil model to forecast where and how soil pH and carbon will change in New South Wales. Tested on sites within Australia, the model will give insight on the drivers of change and will provide a unique analysis of the effect of climate change and land management on the dynamics of soil.   

To find out more, contact Prof Budiman Minasny directly.

Opportunity synopsis:

Soil is a key component of functional ecosystems and is crucial for food, water and energy security, maintaining above- and below- ground biodiversity, mitigating climate change, and has strong links to human health. The project will devise a suite of methods to forecast how soil function changes across space and time to facilitate tactical and strategic ecosystem management

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Project Opportunities

Title: Deep soil assessment : filling the missing link in soil ecosystem services to support agricultural and forestry productivity and resilience to climate change

Summary of opportunity:

This project, in collaboration with CSIRO and industry partners, explores innovative geophysical and modelling methods to map soil thickness —key for understanding how deep-rooted crops and natural ecosystems resist drought, fire, and other climate extremes. You'll gain hands-on experience in cutting-edge digital soil mapping with real-world impact on climate resilience and land management. 

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Project keywords: digital soil mapping, soil depth, forestry 

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Opportunity synopsis:

Soil thickness is one of the key drivers of resilience and productivity in both natural and managed systems, but it is also one of the most difficult soil properties to assess. While national level soil maps have been generated to 2 m, for natural systems and deep-rooted perennials such as tree crops, soil depths between 2 and 10 m can make a huge impact on resilience and productivity, especially under climate change where drought, floods and fires are becoming more commonplace and deeper soils can buffer these systems against extreme climate events. Working with CSIRO and industry partners, this project aims to explore novel geophysical and modelling techniques to assess soil depth in the key rooting zone of 2-10 m, and aim to understand depth to bedrock, as well as depth to hard pan/impenetrable layers and how this influences ecosystem services from soil. 

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