Developing Practical On-Farm Research Tools for In-Season Field-Specific Nitrogen Recommendations

Commodities

Practices

Developing Practical On-Farm Research Tools for In-Season
Field-Specific Nitrogen Recommendations

Context

This project is an integral part of a PhD research plan that
seeks to ultimately provide tools for farmers to be more precise
in N application rates.  The overall PhD research project
has 4 dimensions to it, of which funding is being requested in
this application for item 4:

1. Improved data pipelines for creating datasets at scale for
   use in optimizing Nitrogen Use Efficiency (NUE).  This
   includes aggregating soil moisture sensing, weather data, and
   Small Unmanned Aerial Systems (sUAS) imagery from both university
   plot-level and on-farm research locations.
2. Aggregation of historical datasets for NUE including:
   • Ohio State replicated nitrogen trials from 2013-2023
   • A public 47 site-year dataset from 2014-2016
3. Application of the latest artificial intelligence approaches
   combined with the process-based DeNitrification-DeComposition
   (DNDC) model to provide improved generalized N recommendations.
4. Development of practical on-farm research tools for
   fine-tuning of in-season field-specific N recommendations to be
   trialed with farmers for the 2025 and 2026 growing seasons.

The following graphic describes the overall PhD research plan
with this project content in green:

Outputs

The outputs for this project (from item 4 above) include a
digital tool with 3 features:

1. Generation of prescriptions for replicated sidedress N
   trials.
2. Visualization of yield response to nitrogen rates for a
   specific field to generate economic optimum nitrogen rates
   (EONR).
3. Fine-tuning of an AI based nitrogen model based on
   field-specific data to generate field-specific in-season
   recommended nitrogen rates.

Outcomes

These outputs will enable participating farmers to realize that
nitrogen can be managed more precisely (response-efficacy) and
that the digital tool created enables them to be able to
accomplish it (self-efficacy).  This project will provide
the basis for rolling the tools above out to a larger user base
where the gains in profitability and reduced environmental
impacts can be realized.

Approach / Methods

3 farmers in Western Ohio and a crop consulting and testing
company in Central Ohio will participate in this research
project.  All 3 farmers and the crop consulting company are
both technologically savvy and agronomically knowledgeable and
can provide valuable feedback on the development of a digital
tool that will be valuable for their farming operations. 
The tool will be developed to generate fine-tuning data from the
2025 growing season and to provide improved recommendations based
on that fine-tuned data for the 2026 growing season.

Our primary outcomes of this project are to demonstrate
response-efficacy and self-efficacy for precision nitrogen
application enabled by a well-designed digital tool:

Response-efficacy: Confidence that completing the action will
have the desired effect.

Self-efficacy: An individual's confidence in their ability to
successfully perform an action. 

1. Farmers will have a greater understanding of N dynamics on
   their field and recognize that precision nitrogen management is
   possible (response efficacy).

2. Farmers participating in the project will be confident that
   they can more precisely manage N application using the
   digital tool (self-efficacy).

3. Understanding of farmer interest in scaling the tool beyond
   those participating in this project.

4. Drive further research in academia of aggregating current and
   historical N datasets for AI model training as well as the
   importance of using on-farm research and digital tools for
   fine-tuning AI models in agriculture and drive adoption of
   precision N management practices.

The outcomes listed in this project are enabled by the digital
tool that is intended to provide more accurate nitrogen
recommendations for a specific season and field by fine-tuning a
generalized nitrogen model, and are intended to engage farmers in
a way that they trust the recommendations while also making it
simple to execute the recommendations by creating digital
prescriptions.  From our discussions with farmers, we have
found many to be using flat rates of nitrogen for their fields
and have either been unaware of nitrogen frameworks such as MRTN
or Adapt-N, or have been skeptical that they are appropriate for
their fields.