Theme 2: Agricultural Systems & Catchment Modelling

The USQ Agricultural Systems Research Modelling Research Group conducts research to facilitate practice change on farm, by helping farmers make informed decisions that relate to their production practices, to improve their profitability and social and environmental sustainability.  We provide innovative decision support systems and apps that draw on the power of data, large scale modelling and cloud computing to help farmers and managers make better decisions when they are in the paddock and the office.  

Projects

Project Leader: Afshin Ghahramani/Geoff Cockfield

Research Partner: GRDC |QDAF | The University of Queensland | NSW Department of Primary Industries |University of New England

The project is delivering an economic assessment framework and tool for growers and advisors to evaluate the economics of amelioration options against soil constraints at the paddock and farm scale. The tool, based on a digital platform that emulates similar technologies created by USQ and creates the opportunity for adding further research learnings over time from on-site soil samples. USQ offers considerable leverage opportunities in the soils, technology and economics research area. 

The project looks at investment cost/hectare separate to normal input costs. This allows for a separate cost evaluation of amelioration to allow it to be classed as a farm investment. This may allow for cost to be depreciated over time. Importantly required timeline for anticipated changes is identified and the outlook of residual benefit (treatment stability). This cost will be compared to returns from current yield vs potential yield over time. The investment in dollars can be divided by the expected yield differential multiplied by the market price over time to show if anticipated returns is greater than cost. Such a bio-economic framework, looks to deliver simple cost benefit scenarios to determine break-even point or payback period for farmers and agronomists in the paddock.

Project Leader: Ando Radanielson

Research Partner: ACIAR |IRRI

Developing suitability mapping of integrated rice-fish cropping systems in rice growing areas in Myanmar.

Project Leader: Ando Radanielson

Research Partner: TEIN- Trans EurAsia Internet and netwr

Identifying optimum sowing date in changing climate to improve rice drop productivity and reducing pest and disease risk- Modelling resource use efficiency of integrated rice-fish crop to identify management options for sustainable rice based farming system. 

Project Leader: Keith Pembleton

Research Partner: Qld Department of Environment and Science through the International Water Centre, Griffith University | The University of Queensland |Queensland University of Technology | AIMS | QCIF

Website: https://watermodelling.org/about/our-network

The Qld Water Modelling Network (QWMN) External Engagement Program is helping buid capcaity in water modeling in Queesnland through facilitated engamgent with industry, universities, government and the broader community.

Project Leader: Keith Pembleton

Research Partner: Council on Australia Latin America Relations (COALAR) |Department of Foreign Affairs and Trade|University of Tasmania | University of Buenos Aires | Instituto Nacional de Investigación Agropecuaria (INI A)

This project is assessing how crop-livestock practices impact farm performance regionally under different climate change scenarios. Coupling existing multiple-scaling and remote-sensing techniques with advanced biophysical models we are evaluating drivers of yield variability for forage cropping and pastoral systems in Argentina, Uruguay and Australia.

Project Leader: Keith Pembleton

Research Partner: CRC for High Performance soils | NSW Department of Primary Industries | Federation University | University of Tasmania | Birchip Cropping GroupBurdekin Productivity Services | Western Midlands Group | Riverine Plains Limited

The suite of models and Decision Support Systems (DSS) used in Australian agriculture have a limited ability to represent a diversity of soil constraints and how they interact to impact crop production. Essentially, only nitrogen fertility and soil water dynamics in dryland environments is well represented. Improvements to decision support systems and biophysical model soil components such as phosphorus (a key nutrient) present the opportunity to enhance the current models to provide increased reliability of predictions that can be used in the paddock. Developing soil constraint modules of APSIM and HowLeaky?, (the key analysis engines of several major DSS including ARM online, Yield Prophet and Soil Water App) and then transferring these improvements into their derivative DSS will provide enhanced decision support to the agricultural sector for addressing complex soil productivity/constraint challenges.

Project Leader: Keith Pembleton

Research Partner: Developing Northern Australia CRC | NT Department of Primary Industry and Resources | QDAF | CSIRO | NT Farmers Federation | GRDC | CRDC

Website: https://crcna.com.au/research/projects/potential-broadacre-cropping-nt

This project is collating historical broadacre cropping data, natural resource information and an understanding of market opportunities to support the development of viable broadacre cropping systems in the NT. The initial focus of this project will be on rain-fed and irrigated systems growing cotton and peanut crops, while maize, sorghum, rice and pulse crops will also be investigated as possible 'break crop' options for cotton and peanut producers.  A mixture of on-field and simulation techniques will be used throughout this project. 

Project Leader: Gavin Ash/Keith Pemblton

Research Partner: QDAF | CSIRO | The University of Queensland | AgResearch New Zealand | Iowa State University

Website: https://www.apsim.info/about-us/

This "project" covers USQs contribution to the APSIM initiative.  The consortium of organisations responsible for the promotion, development and maintenance of the Agricultural Production Systems Simulator (APSIM).  APSIM is an international recognised cropping farming systems simulation platform.