Perspectives: Problem-solving and teamwork enhance data efforts

A conversation with Cristine Morgan, Chief Scientific Officer, at the Soil Health Institute

As we approach the completion of the fifth year of the Dairy Soil & Water Regeneration (DSWR) project, data is rolling in, results are being graphed and analyses are underway.

This stage of research is exciting as we see the outcome of years of teamwork, problem-solving and concentrated efforts in the unpredictable environment of row-crop fieldwork. Our baseline soil health survey on commercial dairies is complete, data curation is progressing, and we are starting a new project to use DSWR data to calibrate and evaluate soil carbon, greenhouse gas flux and hydrology models.

Baseline soil survey purpose and findings

The baseline survey asked: What is the state of soil health and carbon on commercial dairies that grow forages? Dairy soils differ from typical row-crop soils due to heavier manure use and machinery traffic during manure application, forage harvest and other field management events.

We collected the Soil Health Institute’s (SHI) essential indicators — soil organic carbon, aggregate stability, carbon mineralization potential and available water holding capacity — across five major dairy regions, from arid Idaho and Texas to humid Wisconsin and New York. As expected, humid soils generally had greater soil health indicator values, but the more interesting findings were management-driven differences within regions.

In humid areas, grazed perennial pastures and fields with more soil health practices (especially cover crops) showed greater soil health indicator values than row crops or conventionally managed fields. In Texas, irrigated row crops had greater soil carbon and mineralization potential but less aggregate stability than dryland ungrazed perennials. In Idaho, corn and alfalfa phases of the crop rotation showed no significant differences. These results, published in the journal Agriculture, Ecosystems & Environment, are also summarized in regional reports, where we found cover crops generally improved soil health more than reduced tillage.

More than 80 participating farmers received results showing how their soils compared to those similar in their region and by management style.

Data management comparison to other SHI projects

DSWR has driven a major expansion of SHI’s data systems. While most SHI projects link farm attributes and soil data, DSWR adds deeper soil profile information, greenhouse gas fluxes, plant tissue and manure analyses, forage yields and continuous soil monitoring. Supporting this scope required extensive documentation, coding and database infrastructure.

SHI created a new system combining a PostgreSQL database with Python-based tools for data extraction, transformation, and validation. This provides a scalable foundation for DSWR data and future projects, while enabling Cloud interoperability and web applications.

Current work focuses on final data curation, documentation and construction of FAIR datasets — findable, accessible, interoperable and reusable. DSWR has produced SHI’s most diverse dataset to date, unprecedented in scale, duration and rigor, and positioned to inform ecosystem service modeling in dairy systems.

Significance of modeling

The modeling component extends DSWR’s impact by testing how well widely used models represent dairy systems. Using baseline survey data, we evaluated COMET-Farm’s ability to model soil carbon on manured soils — a critical step because these models inform policy, conservation and carbon markets.

We are also testing SWAT+, which powers the Fieldprint Calculator, for accuracy in representing soil carbon and water quality changes from soil health management and novel manure products.

Building on this work, SHI was selected for a Foundation for Food & Agriculture Research Seeding Solutions grant to launch Phase I of a new modeling project in 2025. This phase will bring together model developers and experts to compare assumptions across models and evaluate how well they capture processes that drive or respond to soil health improvements.

Photo: SHI’s Dan Liptzin conducts soil sampling