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2017 Projects: Getting the Most out of On-Farm Research

How Can We Get The Most out of On-Farm Research Trials?

To date, the philosophy of strip trial has focused on simple implementation easily integrated into farmer operations.  A similar ethic has predominated in analyzing strip trial data with almost all strip trials analyzed using some forms of classical statistics. This analytic approach fails to fully utilize the extensive, spatial information on crop response across the landscape available in modern strip trial approaches. In this project we seek to use advanced analysis and statistical tools common in other fields to vastly expand the interpretive and predictive power of strip trials across the agricultural landscape to improve our ability to determine risk associated with management alternatives (different rates of fertilizer or manure, timing, method of placement, sources, etc.), and to advance farmer adoption of practices that increase return on investment and reduce the environmental footprint of cash grain and dairy operations. This project will address a problem “of national, regional and multi-state importance” by increasing farm efficiency, profitability and sustainability. Specifically, we will use data collected as part of our crop sensor evaluation project to evaluate the probabilistic predictions of yield response  for a range of management, soil and weather conditions considering variability across regions within the state (and across states), fields and within fields. This project enhances adaptive management on farms and is a collaboration among the University of Missouri, the Iowa Soybean Association, and Cornell University.

If you are interested in participating, contact Quirine Ketterings (qmk2@cornell.edu or 607-255-3061). You can also write to: Quirine Ketterings, Nutrient Management Spear Program, Department of Animal Science, Cornell University, 323 Morrison Hall, Ithaca NY 14853.

Goals

    Our goals are to (1) create a multi-state capacity to use spatial statistics and hierarchical Bayesian statistical methods to analyze data form on-farm strip trials, (2) use existing and on-going strip trials on nitrogen and phosphorus management to deliver probabilistic predictions of yield response  for a range of management, soil and weather conditions considering variability across regions, fields and within fields, (3) integrate the predictive probabilistic estimates into risk-based decision tools to quantify the economic risk or benefits of different management alternatives, and (4) Develop protocols for integrating spatial and hierarchical Bayesian statistical methods as a standard approach for analyzing and interpreting results from on-farm strip trials.

Funding Sources

This project is sponsored by a grant from the Agriculture and Food Research Initiative (AFRI).

Additional Resources

Farmer Impact Stories

Fact Sheets

Extension Articles

Journal Articles

  • Tagarakis, A.C., and Q.M. Ketterings (2017). In-season estimation of corn yield potential using proximal sensing. Agronomy Journal. doi: 10.2134/agronj2016.12.0732.
  • Tagarakis, A.C., Q.M. Ketterings, S. Lyons, and G. Godwin (2017). Proximal sensing to estimate yield of brown midrib forage sorghum. Agronomy Journal (in press). doi: 10.2134/agronj2016.07.0414 .