Market Metrics for Meat: Leveraging POS Data to Improve Farmer Profitability and Farmers Market Success in North Carolina

Commodities

Practices

Farmers selling meat direct-to-consumers face complex pricing and marketing challenges. Livestock must be sold by the piece, with varying yields, costs, and consumer demand across cuts. Although sustainable pasture management reduces dependence on purchased or synthetic inputs, elevated input prices and fluctuations in livestock markets disproportionately burden small producers, highlighting the need for tools that help them measure and maximize sales in their existing market channel to determine what strategies offer the greatest profitability.

At the same time, farmers markets remain a cornerstone of local food economies but lack reliable data on what drives vendor success. Most price indexes rely on limited, self-reported prices, without verifying what customers actually paid. Without credible benchmarks or shared data, both farmers and farmers market managers struggle to make informed decisions that improve sales and reinforce sustainable practices.

This pilot project addresses these gaps by applying a systems-based, data-driven approach to improve farm and market performance across North Carolina. NC Choices, NC State Extension, and NC A&T will collaborate with Cornell to adopt and expand their New York research, working with 10-15 pasture-based farms, 5-7 farmers market managers, and disseminating tools to 250+ livestock farms statewide. Using Point-of-Sale (POS) data, the project will identify the primary determinants of customer transaction size and daily sales volume across farms and markets. Regression models compare thousands of individual sales to identify which farm-level factors (e.g., item value, price, timing) and market-level factors (e.g., vendor mix, management structure, demographics) are most strongly linked to spending, while accounting for the influence of multiple variables.

Participating farms will access easy WordPress-based tools developed by Cornell, including the Market Metrics Dashboard, providing real-time performance insights, aggregated findings, and the Price Report Portal, benchmark pricing for pasture-based beef, pork, chicken, and lamb. Farmers and market managers will receive synthesized findings on how markets and farm-level marketing influence vendor sales, enabling data-driven decisions to influence consumer spending.

The educational component connects farmers, market managers, and researchers through a continuous feedback loop. Blacklin (NC Choices) manages the project and leads market manager outreach, Menius (NC Choices) provides technical assistance to farms, and co-leads educational outreach, including cohort orientations, mid-project peer exchanges, and regional workshops. Matt LeRoux (Cornell) will lead the econometric modeling, training, and statistical methodology, partnering with Dr. Raymond Thomas (NC A&T) to implement and analyze the NC data, while Dr. Hannah Dankbar and Dr. Dara Bloom (NC State Extension) will support broader market analysis and farm-based evaluation and case study development. All contribute to dissemination through respective Extension networks, professional conferences, and publications. Farmers will co-present findings, reinforcing farmer-driven communication.

By linking economic impact, this project strengthens long-term agricultural viability. Profitability enables continued investment in pasture-based systems that build soil health. Collaboration and transparency enhance community well-being and the stability of local markets. Integrating farmer behavior, market governance, and consumer demand ensures that improvements at one level of the system, such as vendor profitability, support the entire local food network.

The following objectives integrate applied research and farmer-led education to strengthen the food system. This project moves from identifying what drives sales to testing how farmers and market managers use data to make decisions and adapt strategies over time. The four objectives move sequentially from data collection and analysis, to farmer education and strategy testing, to monitoring and broad dissemination of results.

1. Farmer Engagement and Data Collection: By the end of Year 1, this project will have enrolled 10-15 pasture-based livestock farms and 5-7 farmers market managers in standardized POS data collection, establishing a transaction-level dataset sufficient for regression analysis. Data collection will span multiple market seasons and include market-level variables such as vendor mix, events, and governance structure, as well as community-level demographic indicators. The data will also include baseline and case study interviews with participating farms to document current practices, business goals, and changes over time related to pricing, marketing, and operational decisions.
2. Model Development and Analysis: Identify the primary determinants of customer transaction size by integrating POS data with farm-level and market-level factors, producing findings that help farmers and market managers understand what drives sales. Build on Cornell's foundational research by incorporating additional market-variables and community-level influences, adapting the model to regional market conditions. Maintain and expand the Market Metrics Dashboard as an ongoing decision-support tool for participating farms, and produce aggregated price reports and performance benchmarks for wider farmer use.
3. Education, Technical Assistance, and Testing: Provide onboarding, technical assistance, peer sessions, and pricing trainings for the livestock farming community in NC; support participating farms in testing at least one farmer-controlled strategy, such as a pricing adjustment, product mix change, or merchandising improvement, and measuring its effect on customer transaction size. Train participating farmers to use the Market Metrics Dashboard to monitor performance, identify target metrics, and guide decision-making. Facilitate peer-to-peer learning through cohort exchanges and feedback loops to refine strategies based on real-world experience.
4. Monitor Impacts, Refine, and Share Results: Monitor outcomes to assess if and how farmer-controlled changes impact customer transaction size and overall farm performance. Use pre/post case studies to evaluate changes in farm business outcomes, decision-making confidence, operational efficiency, and alignment with broader farm goals. Refine tools, variables, and recommendations based on findings and participant feedback. Share results through Extension trainings, online dashboards, and outreach to inform pricing and market governance. Farmers will co-present findings to peer networks. Findings will be shared with both farmer networks and with farmers market managers to promote farm business and market-level changes. Together, these findings support a replicable model for data-driven decision-making in direct-market livestock systems across the Southern region.