Most agricultural research happens on farms. Farmers provide the land, the labor, and often the local knowledge that makes a study possible. But the questions that drive the research, the choices about what data to collect and how to analyze it, and the decisions about what findings mean and who they reach have traditionally stayed with researchers. Conventional agricultural research positions farmers as subjects and consumers of results, reserving the researcher role for those with formal scientific credentials. That arrangement produces knowledge that often fails to reach farmers, fails to address what farmers need, and fails to persist after the grant cycle ends (Nelson et al. 2023).
Participatory agricultural research promises farmers’ shared authority over what gets studied, how, and for whom. Participation only means something if farmers are in the room when questions get set. At Our Sci, we’ve built tools and workflows alongside farming communities long enough to have a clear view of what makes participation real, and what makes it collapse.
Researchers tend to arrive with a research question and recruit farmers to answer it. Instead, we recommend spending enough time in a community to understand what questions they already ask.
Legitimate insight comes from inside a community’s knowledge ecosystem, from understanding its ecological conditions, cultural norms, and constraints. When Our Sci partnered with Point Blue Conservation Science to support development of the farmOS: Conservation Planner, we started by listening and understanding the fieldwork. We surveyed dozens of conservation planners across the US to understand how they currently work: paper forms for site assessments, Google Earth or QGIS for maps, spreadsheets for data, and Word documents for the final plan. That embedded understanding revealed that most available software was priced out of reach for conservation planners and built for workflows that bore no resemblance to theirs. The tool followed the community.
The evidence on why this matters (Bindu Poudel-Ward 2024): 86% of farmers cite limited time as a primary deterrent to research participation, 69% cite complicated protocols, and 60% cite data privacy concerns. Protocols built without farmer input create exactly this burden. Protocols designed collaboratively fit the farm from the start, stay simple enough to use, and earn farmer trust before the first datapoint.
Face-to-face, community-embedded knowledge transfer is the mechanism through which findings become practice. Technology supports that infrastructure; it accelerates it.
The Pasa Sustainable Agriculture Soil Health Benchmark Study demonstrates this concretely (see Pasa’s Study). Pasa collected soil data from hundreds of farms across Pennsylvania and Maryland and built quarterly cohort calls, an annual workshop, and custom farm-level benchmark reports that every participating farmer received and could share. Farmers displayed reports on farm websites, distributed print copies to wholesale buyers and CSA subscribers, and used them to tell their soil stewardship story to neighbors. The result: 92% of farmer collaborators returned to submit data year over year, and every farmer who completed a survey rated the experience good or excellent.
That retention reflects a reciprocal relationship. Farmers who see their data return as useful knowledge, in a form they can read, share, and act on, become ambassadors for the research. Each farmer who shares their benchmark report with a neighbor extends the study’s reach further than any outreach campaign could.
Farm research data workflows collapse for a predictable reason: data accumulates in mismatched formats, and cleaning it absorbs the time that should go to analysis. When Pasa approached Our Sci about the Soil Health Benchmarking Study, they were collecting thousands of data points from dozens of different sources and formats, overwhelming staff time at the expense of knowledge generation.
The answer was a standardized database architecture designed to make data analysis-ready from day one: consistent schemas across all farm activities, quality control workflows for data validation, translation systems to normalize diverse input formats, and storage structures following the Common Farm Convention standards so data moves across tools and between institutions without manual re-entry.
The right infrastructure removes institutional access as a prerequisite for participation. While partnering with the Bionutrient Institute to support their nutrient variability work, Our Sci built SurveyStack to connect directly to the Reflectometer via Bluetooth on Android, so a producer volunteer with no lab access could collect crop measurements that flowed directly into an automatic analysis pipeline. More than 10,000 crop samples from producer and consumer volunteers flowed through that system at a fraction of the cost of traditional lab-based studies. The hardware runs under GNU GPL v3.0, the software under open-source license, and all data published openly on GitLab. These are deliberate technical choices that determine who can use, adapt, and maintain the system without asking permission.
Farmers who contribute data must know exactly how it will be used, who owns it, and how to withdraw it. Participation collapses when data sovereignty does.
A 2025 systematic review of agricultural data ownership across 63 peer-reviewed studies (Berisha et al. 2026) concluded that “although farmers generate vast quantities of nonpersonal data, no existing legal framework explicitly grants them ownership, leaving ownership to be ambiguously allocated or de facto transferred through contracts in ways that limit their ability to contest access or downstream use.” Agricultural technology firms routinely assert proprietary rights over farm data through platform contracts. Opaque data practices mean farmers share data without clear information about who can access it or how it affects their eligibility for subsidies, insurance, or credit.
Farmers have expressed concerns about privacy, agency, and extraction of their data captured by external organizations. These concerns reflect real experiences of farmers participating in research programs and watching the outputs benefit someone else.
Data governance questions need answers before data collection begins: Who owns the raw farm data? Who can access derived insights? Can a farmer remove their data? Who benefits if the data generates commercial value? Our Sci’s approach rests on two commitments: open-source infrastructure that any community can maintain, and data governance that stays with the organizations who collect it. Meaningful sovereignty requires three interlinked pillars (Berisha et al. 2026): enforceable baseline rights for farmers, accessible (open-source) infrastructure, and participatory governance arrangements.
The measure of a well-designed tool is a community that eventually outgrows it. Knowledge that once required software support becomes embedded in practice, passed peer to peer, and maintained without outside technical help. That’s the goal.
Our Sci’s SoilStack platform points toward this model. A producer with no specialized equipment and no university research station relationship can run design-based soil sampling independently, using publicly available digital soil maps and GPS navigation. A user learns the method from the process. A farmer who completes a season with SoilStack understands the underlying design logic, not just the interface.
The self-obviating model assumes the community will carry the knowledge forward. When the tool is the connective tissue of a network, longevity matters more than exit.
The farmOS: Conservation Planner reflects a related but distinct commitment: infrastructure that doesn’t collapse when the grant cycle ends. Built on farmOS, an open, flexible platform with an active support community and a farm-native data model, it’s transitioning from grant-dependent funding to a community-supported model. Farming communities facing boom-or-bust funding cycles need infrastructure they can sustain themselves. Refuse extractive complexity: if a sociotechnical system creates more burden than it relieves, the right answer may be not to build it. Simplicity, modularity, and community maintainability are virtues.
Every tool, every workflow, and every research process should move toward equal access to knowledge, resources, and the power to shape the systems people depend on. That goal outlasts any single project.
Design for the specific community in front of you, its ecological conditions, resource constraints, cultural norms, and existing knowledge infrastructure. The Conservation Planner was built for conservation planners navigating government funding bureaucracies in California. SoilStack was built for producers monitoring soil health changes across varied fields without grid-sampling budgets. The Reflectometer was built for the distributed volunteer networks of the Bionutrient Institute. Each tool carries the fingerprint of a specific community’s needs.
Participatory research that actually works starts before the first survey and continues after the last data point. It builds relationships before it builds systems, returns data before it publishes findings, and designs for the moment when the community no longer needs outside technical support. That’s the moment the work has succeeded.
Our Sci builds open-source data infrastructure, tools, and workflows for farm organizations and research networks. If you’re working on a participatory research project and want to talk through the design, get in touch.
