MaiaGrazing:
Graze planning tool re-design
MaiaGrazing’s mission is to help farmers balance livestock productivity with sustainable land practices. The existing graze planning tool was complex and relied heavily on customer support.
As Lead Product Designer, I led the end-to-end design process within a cross-functional team, transforming farmer insights into a streamlined tool, resulting in a 30% faster setup and increased engagement.
Project goals
My goal was to simplify the experience and encourage farmers to prioritise land health while still meeting their livestock goals. The tool needed to go beyond managing grazing schedules.
Demonstrate that prioritising land health boosts yields and resilience alongside livestock goals.
Simplify setup and reduce reliance on customer support, empowering farmers to use the tool independently.
Enable farmers to adjust plans in real time, making the tool a decision-making partner, not just a planner.
The process
This project followed the Double Diamond process to ensure a structured approach. Each stage was grounded in real-world farmer feedback, ensuring the tool remained relevant and practical.
Discover
Understanding what matters to farmers
Through interviews, on-farm visits, and collaboration with the Customer Success team, I came to understand what drives farmers’ decisions and gained insight into their seasonal pressures and need for quick, actionable decisions. I found that the tool’s complexity didn’t align with these practical needs.
A key insight was that farmers found data entry tedious without immediate value, prompting us to explore ways to streamline input and provide instant feedback or projections.
As a next step, I collaborated with the data team to map essential graze planning data. This helped me identify opportunities for automation to reduce manual entry, enhancing both accuracy and adaptability.
Define
Framing the problem
Insights from the discovery phase allowed me to refine and synchronise our findings into a clearly defined problem statement, capturing the core challenges farmers faced.
This statement laid the foundation for our design process, ensuring we addressed real needs.
Building on this, I crafted targeted ‘How might we…’ questions to guide our approach. These questions ensured each design decision aligned with both farmers’ needs and MaiaGrazing’s sustainability goals.
Develop
Iterating to a solution
In this phase, I focused on creating prototypes and refining features based on continuous feedback from farmers, each iteration was designed to improve usability and address our HMW questions.
Feedback from farmers using the existing form-based tool revealed that it was too rigid and didn’t align with their unique goals, making it challenging to adjust plans as conditions changed. This disconnect limited the tool’s overall effectiveness and ease of use
1st Iteration
Initially, we introduced a wizard approach that guided farmers through a series of steps, beginning with a key decision: whether to prioritise paddock (land health) or animal-first (livestock needs).
This approach helped us test key points in the process but ultimately proved too lengthy and cumbersome for farmers.
We also discovered that the choice between paddock-first and animal-first was too broad and confusing. Farmers expressed a need for more guidance and clearer starting points, so we shifted our focus to preset goals that better aligned with their common objectives.
2nd Iteration
We replaced the two option approach with preset goals like “Rain ready” or “Got to market.” These predefined goals provided farmers with clearer starting points.
The aim was for the plan to automatically provide the optimal route based on factors such as seasonal changes, forage availability, and paddock recovery needs, tailoring itself to the selected goals.
This iteration allowed us to test how well farmers responded to having these preset goals without needing to navigate complex technical details.
3rd Iteration
In previous iterations, we displayed the map view of the graze plan as a final step. Farmers found it extremely helpful but wanted it to be more dynamic.
We then made the map the central focal point of the tool, enabling farmers to see the graze path dynamically change in real time as they adjusted their inputs.
This shift made the tool more intuitive and visually engaging, providing immediate feedback on changes and enabled farmers to make more proactive adjustments during the planning process.
We also added a data completeness gauge to encourage farmers to input key data, improving the accuracy of recommendations.
Adapted for mobile
We focused on reducing data entry delays by optimising the tool for mobile, bringing essential features directly to the field.
Farmers could now access their planned grazing routes, see progress, receive real-time suggestions and alerts on changing conditions and key events, and be prompted to log paddock data when moving livestock.
This enabled real-time decision-making, improved land monitoring, and provided more accurate data for future grazes, all while reducing support needs.
Deliver
The final tool and its impact
The re-designed tool provides a comprehensive, adaptive experience that empowers farmers to make independent and sustainable grazing decisions.
By involving engineers early in the process, we streamlined development, ensuring that each feature was built and delivered efficiently and aligned seamlessly with farmers’ practical needs.
Projected Outcomes
30% faster setup
A simplified onboarding process means farmers can set up and start using the tool more quickly.
Increased engagement
Real-time suggestions and event alerts encourage ongoing interaction and make it easier for farmers to follow through with sustainable practices.
Environmental benefits
The tool encourages practices that promote soil health, helping to prevent erosion and support carbon sequestration.
Key takeaways
This project challenged me to simplify complex processes into intuitive, user-friendly solutions, reinforcing how a streamlined approach empowers users to make independent decisions.
Real-time feedback and iterative design kept the tool adaptable and user-focused, ensuring it grew with changing needs and added real value for both the business and farmers.