Digital Agriculture Services

Working with a senior product manager, we were able to identify that grain traders across Australia face the challenge of obtaining current and historic cropping information. These insights are crucial for effectively managing commodities on the world market, enabling traders to make informed decisions and mitigate risks associated with fluctuating supply and demand dynamics.

Data gathering: To ensure we understood the size of the problem and to ensure we would release a viable product for the initial release, we conducted a series of interviews with a range of different grain traders and operational managers across multiple organisations such as Cargill, CHS Broadbent, Grain Corp and CBH totalling to 10 participates.  

Insights: We identified five themes:
‍
•The data accuracy has to be at least 85% to be useful insights that grain traders would feel comfortable with using in their role.
‍
•Crop growth is dependant two key factors of weather and temperature to be able to project crop growth accurately.  
‍
•Historical weather and temperature information would allow grain traders to compare year on year data to analyse patterns in crop growth.

•The current method to set pricing for future grain commodities, requires people scouting on the road or commissioned aerial imagery of competitor farms, resulting in substantial costs.

• Traders will use multiple different platforms, such as Elders weather, Google Earth, Microsoft Excel.        

Competitor analysis: Although there are a few geospatial cropping companies globally, such as Onesoil, and EOS, they don't accurately service Australia making the data inaccurate to be useful.

Given the complexity of data modeling in the project, we needed to establish the architecture for the initial release of Grain intelligence , as this would significantly impact user experience in terms of searching and filtering. These sessions involved collaboration between the engineering team and DAS stakeholders to align expectations and define the technical scope effectively.

The next step was to understand how grain traders work. To achieve this, I re-engaged with the initial grain traders who participated in the interviews, shadowing them to capture their behaviours, and workflow and interactions with other software and design scope developing several low-fidelity wireframe concepts as part of the design evaluation process, and finalising the preferred concept.  

All concepts were thoroughly discussed and presented to both engineering and DAS stakeholders to ensure the feasibility of the proposed ideas. This preliminary step was essential before progressing to the development of high-fidelity designs. Subsequently, an assessment of the project's components was conducted, alongside careful consideration of color contrasts for any visual elements such as maps.

By integrating geospatial data, the portal provides detailed maps showcasing the location of farms and the specific crop types being cultivated in each area, organised by local government areas. Additionally, we incorporated real-time weather reports into the portal, enabling grain traders to stay informed about current and forecasted weather conditions across different regions. This comprehensive platform equips grain traders with the insights they need to make informed decisions about commodity management and navigate the complexities of the grain market with confidence.

We initially launched a small beta release to 5 users across 3 companies. This beta group reported an average revenue increase of 16% in grain/cropping trades during the first quarter of release. However, the most significant benefit of using the Grain Intelligence platform was the time saved in accessing historic information, which notably built confidence in the product.