A Digital Agronomy Approach to Farmer-Centric On-Farm Soil Health Experimentation in New York Grain Systems

Project Overview

ONE26-480
Project Type: Partnership
Funds awarded in 2026: $29,997.00
Projected End Date: 04/30/2029
Grant Recipient: Cornell University - Farmers DataLab
Region: Northeast
State: New York
Project Leader:
Louis Longchamps
Cornell University

Commodities

  • Agronomic: corn, soybeans, wheat

Practices

  • Crop Production: cover crops
  • Education and Training: on-farm/ranch research
  • Soil Management: soil quality/health

    Proposal abstract:

    Soil health is foundational to the long-term productivity and resilience of Northeast farming systems, yet farmers working in grain and cover crop rotations often lack accessible, farm-specific data to guide management decisions around cover crop selection, tillage, and soil organic matter. Existing research provides general guidance, but it rarely captures the variability in soils, climate, and management that defines farming in New York and the broader Northeast. As a result, farmers experiment on their own terms but without the analytical support needed to learn reliably from those experiments.

    This project will use digital agronomy to enhance and de-risk farmer-led on-farm experimentation (OFE) focused on soil health practices in grain and cover crop systems, including corn, soybean, and wheat rotations. The central hypothesis is that combining farmer-led experimentation with systematic data collection and analysis can generate farm-specific, actionable knowledge about cover crop species and mixtures, tillage and soil disturbance, and soil organic matter and nutrient dynamics, while building a multi-farm network that reveals broader trends across the region.

    A network of at least ten New York farms will be recruited, with each farmer designing and conducting their own experiment according to their own interests, equipment, and capacity. The Farmers Data Lab at Cornell University will support each farm by collecting agronomic, environmental, and remote sensing data alongside the farmer's own trial, organizing the data into a shared database, and generating both farm-level and network-level results reports. Field equipment is owned by the lab, sample analysis costs are covered in the project budget, and graduate student effort is supported through Cornell teaching assistantships and existing grant funding. Farmers and the project team will then interpret results together in a collaborative workshop, connecting individual outcomes to regional patterns and planning subsequent rounds of experimentation.

    Results will be shared through farm-level reports delivered directly to each participating farmer, a synthesis report summarizing network-wide findings, a network-level results workshop and extension publications targeting New York grain farmers and agricultural service providers. This project aligns with SARE's legislative priorities by directly working to maintain and enhance soil quality and productivity, conserve natural resources, and support the economic viability of Northeast farming operations.

    Project objectives from proposal:

    This project asks whether digital agronomy tools and methods can enhance the quality and reliability of farmer-led on-farm experimentation focused on soil health practices in New York grain and cover crop systems, and whether doing so generates actionable, farm-specific knowledge that farmers can apply to subsequent management decisions.

    Objective 1: Recruit and establish a network of at least ten New York grain farms actively experimenting with one or more soil health practices, including cover crop species and mixture selection, tillage and soil disturbance, and soil organic matter.

    • Key performance indicator: At least ten farmers have completed an experimentation registration form documenting their experimental objectives, hypothesis, design, and crop management context by spring 2027.

    Objective 2: Generate a comprehensive, multi-farm database integrating farmer management data, agronomic and soil health sampling, remote sensing, and environmental covariates for each experimental site, building on an existing OFE database structure developed through prior collaborative work with New York grain farmers.

    • Key performance indicator: A complete database is assembled for all participating farms, with farm-level results reports delivered to each farmer and their agronomist within one growing season of data collection.

    Objective 3: Assess whether digital agronomy-enhanced OFE generates results that farmers find actionable and intend to apply in subsequent seasons.

    • Key performance indicator: A network-level results workshop is conducted with all participating farmers and their agronomists, with structured exit surveys and facilitated discussion documenting farmer responses to their results, perceived usefulness of the farm-level reports, and intended management changes for the following season.
    Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and should not be construed to represent any official USDA or U.S. Government determination or policy.