Goals and Objectives

Building Collaborative Research Networks to Advance the Science of Soil Fertility: Fertilizer Recommendation Support Team (FRST) Project

The Fertilizer Recommendation Support Team is a national initiative dedicated to improving the scientific foundation of soil fertility and fertilizer recommendations across the United States.

FRST addresses long‑standing soil fertility and soil testing challenges by promoting innovative, consistent, and research‑driven approaches to fertilizer recommendation development. Core efforts include:

  • advancing soil test correlation and calibration,
  • modernizing soil fertility data and research methods, and
  • expanding coordination among research, Extension, industry, laboratories, producers, and other key stakeholders nationwide.

FRST also curates and maintains the national soil test correlation database that supports the FRST Decision Aid.

The FRST Decision Aid is an online, science‑based tool that helps agronomists, researchers, and producers interpret soil test results using the best available correlation and calibration information from across the United States. It provides transparent, standardized fertilizer recommendation guidance based on nutrient, crop, soil test extractant, and geographic region.

FRST is the eleventh National Research Support Project (NRSP11) supported by agInnovation, the Agricultural Experiment Station Directors. NRSPs enable high‑priority, national‑scale research by connecting scientists and stakeholders across universities, USDA, and industry.

To join NRSP11, visit the National Information Management and Support System homepage or reach out to one of the NRSP11 contacts. Explore this website to learn more about FRST Project activities, tools, and ongoing research efforts.

Mission

The Fertilizer Recommendation Support Team’s mission is to advance agronomic nutrient management by making soil-test correlation and calibration research data accessible for developing innovative and science-based fertilizer recommendations that are consistent, adaptable across regions, and augment existing nutrient management systems for public and private stakeholders.

Goals

  1. Facilitate a Community of Practice
    Facilitate a community of practice by building and sustaining a collaborative network of public research and extension professionals, agronomists, and industry partners to share knowledge, harmonize methodologies, and co-create soil-test-based fertilizer recommendations that advance sustainable nutrient management.
  2. Catalyze Innovative Nutrient Management
    Catalyze innovative nutrient management by promoting the development and adoption of science-based fertilizer recommendations and technologies that improve nutrient use efficiency, crop productivity, and environmental sustainability.
  3. Enable Data-Driven Decision Making
    Enable data-driven decision making by preserving, managing, and analyzing comprehensive soil-test response data using consistent and innovative modeling approaches to support transparent, reproducible, and regionally adaptable nutrient management strategies.
  4. Support Public and Private Stakeholders
    Support public and private stakeholders by providing user-friendly access to FRST data, decision-support tools, insights, training, and outreach to complement and enhance nutrient recommendation systems used by universities, government agencies, agribusinesses, independent consultants, and farmers.
  5. Facilitate Cross-Regional Collaboration
    Facilitate cross-regional collaboration by supporting the harmonization of soil fertility recommendations across regions when scientifically appropriate, to improve consistency, scalability, and transparency in nutrient management practices while respecting regional agronomic differences.

Objectives

  1. Cultivate a Community of Practice
    Establish regular forums, workshops, and collaborative platforms for stakeholders to share data, methodologies, and insights, and encourage co-development of tools and recommendations through inclusive engagement across disciplines and sectors.
  2. Develop and Promote Innovative Nutrient Management Tools
    Create and refine decision-support tools that translate soil-test data into actionable fertilizer recommendations, and integrate emerging technologies and modeling approaches to improve nutrient use efficiency and sustainability.
  3. Preserve and Analyze Soil-Test Response Data
    Archive historical, current, and future soil-test response data to ensure long-term accessibility, and apply consistent and innovative modeling techniques to analyze data and support reproducible recommendations.
  4. Engage and Support Diverse Stakeholders
    Collaborate with public institutions, private industry, and independent consultants to ensure tools meet practical needs, and provide training, documentation, and outreach to facilitate adoption and integration of FRST outputs.
  5. Enable Scientifically Justified Regional Harmonization
    Identify opportunities for harmonizing fertilizer recommendations across regions based on scientific evidence, while respecting regional agronomic differences and promoting transparency and consistency in nutrient management.

Activities & Aspirations

Activities

  1. Survey land-grant institution soil-test-based recommendations to understand the complexity and variation of existing recommendations.
  2. Develop standardized terminologies for use in soil-test-based nutrient management recommendations that enhance end-user understanding and adoption of soil testing.
  3. Establish nutrient-specific minimum data requirements for legacy phosphorus and potassium data and for ongoing/future correlation-calibration study data to standardize best practices and enhance data use and reuse.
  4. Develop a database that provides a clearinghouse for soil test correlation and calibration data that is populated with legacy data and data from ongoing soil-test correlation and calibration studies for major field crops grown in North America. The database should adhere to FAIR Principles and meet the minimum data requirements.
  5. Develop a searchable, decision support tool that provides soil test phosphorus and potassium correlation and calibration analysis output (graphs and statistical confidence intervals) based on queryable terms such as geographic area, crop, yield range, soil-test method, soil sample depth, soil series, etc.
  6. Establish benchmark methods for calculating relative yield and modeling soil test correlation and calibration data.
  7. Survey stakeholders to better understand decisions on soil analysis and use of soil test data in actionable practices at the farm level.
  8. Examine the variability of soil pH methods, lime requirement methods, and lime rate recommendations.
  9. Evaluate the effect of lime source and incubation time on soil pH response to liming.
  10. Calibrate lime rate recommendations using a diverse set of soils from the USA to promote accurate and consistent lime recommendations across geographic areas in the US.
  11. Develop minimum dataset guidelines for soil-test sulfur correlation and calibration, and add sulfur as the third nutrient in the national database and decision support tool.
  12. Identify soil-test correlation and calibration data gaps to guide future research and technology.

Aspirations

  1. Incorporate the probability of response into soil test correlation analyses through innovative modeling.
  2. Incorporate return on investment on soil-test-based fertilizer recommendations into soil test correlation and calibration modeling to inform stakeholders of the profitability of fertilization metrics.
  3. Advance our understanding of the relationships among soil depths for estimating soil test data from soil depth increments, predicting soil test values at unsampled depths, and identifying the key drivers of soil stratification.
  4. Incorporate equations into the decision aid to convert soil test nutrient concentrations between extraction and analytical determination methods that are highly correlated to expand the amount of data available for soil test correlation and calibration analyses.
  5. Develop complex soil test correlation models that use multiple soil-test variables to more accurately predict crop response to fertilization than single nutrient concentration correlation.
  6. Use meta-analysis to identify physiographic regions that can share data-driven, soil-test recommendation logic.
  7. Investigate specific soil test correlation and calibration relationships that consider crop nutrient needs for different yield goals and soil nutrient buffering capacities (e.g., soil texture and organic matter content).