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The question of who exactly counts as a farmer might seem straightforward, but for technology developers, the answer is anything but. According to Danny Donaghy, professor of dairy production systems at Massey University, defining the end user is critical when designing agricultural tech—because the person making decisions isn’t always the one in the paddock.
Is the farmer the owner, the manager, a herd supervisor, or even a corporate board member? Each role demands different information, delivered in ways that fit their workflow. Donaghy’s point is simple: if developers don’t ask *who* needs *what* data—and *how*—their tools risk gathering digital dust. “It keeps coming back to who needs what information, in what format, and when?” he says.
The challenge isn’t new. A 15-year-old Australian study on dairy farmers’ tech adoption found many weren’t interested in data-heavy solutions—they just wanted time-saving tools like automatic teat sprayers or cup removers. Today, while sensors and dashboards have proliferated, the core demand remains: technology must cut costs, save effort, or boost efficiency. Precision fertiliser application, for example, ticks those boxes. But if tools don’t integrate—forcing users to juggle multiple apps—they create more work than they solve.
Where could tech make the biggest difference? Donaghy highlights three areas: measuring and allocating feed (pasture, crops, or fruit), monitoring animal health in real time, and using sensors to manage soil and water. Currently, checking soil moisture or nutrient levels manually is laborious. Networked sensors could streamline this, offering instant, regulator-ready data while freeing up farmers for higher-value tasks.
Yet the risk of data overload looms. James Allen, chief executive of agribusiness consultancy AgFirst, warns that precision farming—managing every square metre and animal individually—will require vast amounts of data. The payoff might not always be financial; sometimes, it’s about smarter decisions, like adjusting grazing patterns based on live soil conditions or catching health issues in livestock before symptoms appear.
Allen notes that IT companies are becoming more open to sharing data, but collaboration won’t happen overnight—or for free. The real test for developers? Ensuring their tools don’t just *collect* data, but *distill* it into actionable insights. For farmers, the message is clear: start with the problem, not the tech. Define what needs fixing, then find the tool that fits—not the other way around.
The shift toward data-driven farming is inevitable, but its success hinges on one thing: making sure the right information reaches the right person, in the right way. Otherwise, even the smartest tools will struggle to find a home in the shed.