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Theme 4: Agrimetrics

Agrimetrics refers to the use of both existing and newly derived data from sensor technologies to improve our understanding and optimisation of complex agricultural systems. It harnesses the latest in soil and crop sensor technology, machine learning, geostatistical methods and process modelling to produce data capturing detailed information on soil and crop status. This synthesis will support the adoption of sustainable farming practices, such as precision agriculture, that improve the efficiency, profitability and sustainability of agricultural production. It will provide the information needed to improve our understanding and modelling of agricultural processes. Information gaps and future data requirements will also be identified to inform the development of the next generation of sensor technology.

Project

HowLeaky Panel and Operational Support Project

Project Leader: Afshin Ghahramani

Research Partner: Qld Government - Queensland Department of Environment and Science |Department of Natural Resources, Mines and Energy

Website: www.howleaky.com
                   
HowLeaky is an important asset for the Queensland Government, universities, research institutes and is useful for estimating water balance and water quality for a range of land uses and management options. 

It is applied to benchmarking water quality changes associated with the Great Barrier Reef catchment investments. This project has developed a new web-based platform for the HowLeaky model to facilitate improved management, development and governance of the model. This was delivered through a collaborative research agreement between the University of Southern Queensland and the Queensland Government, Department of Science and Environment.

A peer review and support framework including a HowLeaky Steering Committee and Panel were developed to oversee model peer review activities and governance. The Panel includes members from the Queensland Government, the University of Southern Queensland and the private sector. The activities of the Panel are overseen by a Steering Committee which consists of the delegate(s) from the Queensland Government and the University of Southern Queensland. The Steering Committee ensures on-going support for the development and use of HowLeaky.

Project Leader: Craig Lobsey

Research Partner: Soil CRC

This is a collaborative project with Manaaki Whenua Landcare Research (NZ) and industry partners to develop novel soil sensing technologies.  With the sensor technology and analytics we are developing in this project we aim to provide land managers with more detailed, cost effective and timely information on soil nutrient status and dynamics. 

Project Leader:  Afshin Ghahramani 

Project Partners:  North QLD Dry Tropics

The project will provide preliminary soil health data on the effect of regenerative grazing practices on Glenalpine Station, through testing of some biological indicators of soil health, and provides additional data for monitoring of the livestock treatment of the RTIV Stomping out Sediment project at Strathalbyn Station. 

Project Leader: Afshin Ghahramani

Research Partner: GRDC | The University of Sydney | CSIRO | Australian National University| BoM

This project will deliver a scientific framework that can be used by digital agriculture companies and commercial third parties to nowcast plant available water (PAW) at any point in time, within and across paddocks and at multiple depths in the soil profile to aid growers with on-farm decision making. The approach will be agnostic to the type of soil water data streams and will be able to digest these to extract the best features of all in terms of accuracy and spatial and temporal resolution to provide improved predictions of PAW using scale-able, modular modelling frameworks that can be easily operationalized into new analytics products by commercial third-parties. The agnostic nature of the approach means that it will be future proof in terms of being able to accommodate the next generation of sensors, remote sensing platforms and water balance modelling approaches using a foundation of data layers that are freely available third-party IP but also being flexible enough to incorporate additive value generated from sources of commercial TIP. The project will test, develop and refine data-driven, data assimilation, hybrid soil water balance model and ensemble-based approaches (i.e. different analytical frameworks) to predicting PAW using the combined expertise of five different research organisations and strong collaborations with grower networks and industry, including the Society for Precision Agriculture in Australia.