Variability in farm soils, landscapes and climate make effective and efficient management decisions complex, impacting the ability of farmers to maximise their return on investment and minimise risk.
Growers have access to their farm data through existing tools (e.g. John Deere Ops Centre, SMS, PCT cloud etc) and the ability to generate maps for precision agriculture.
However, these maps are not generated using a scientifically robust method and there is no existing tool for designing or analysing strip trials that agronomists and skilled growers can use to achieve this.
Furthermore, growers are looking to improve their sustainability outcomes and reduce emissions.
This project seeks to develop a research-based analytics and decision support tool that will allow farmers and their advisors to increase profitability and optimise risk management.
The Agri-analytics Hub will allow visualisation and analysis of within paddock variability in crop performance and profitability. It also enables users to robustly plan, implement and analyse the outputs of alternative management practices. The Hub will enable users to extend results of experimental outcomes to similar land management units. Collectively this capability will enable farmers to more readily engage with spatial variation in crop performance, to test solutions and then to estimate the likely economic impact of adoption based on results obtained from their own production system.
The hub will be accessed through an intuitive, easy-to-use, cost-effective and scientifically rigorous tool that fits into agronomists' existing systems and processes.
WA Node Leader at Food Agility CRC
Curtin 4 Agribusiness Profitability (C4AP) Initiative Lead at Curtin University WA