Improving Nitrogen Efficiency in Corn Production Systems with Data and Modeling

Commodities

Practices

Corn (Zea Mays L.) is grown in almost all parts of the United States and is the largest consumer of nitrogen (N) fertilizer. Traditionally, N application rate and time are recommended more at the county or regional level using the regionalized mass-balance equations or through the expected economic returns. With some risk aversion, farmers usually apply more than the required amount of fertilizers in their fields and these can exacerbate environmental, economic, and social issues. Research has been conducted in the past to overcome these issues, such as field experiment trials or using remote sensing. These methods are time-consuming, costly, and labor-intensive and do not evaluate environmental sustainability. Another method would be to deploy crop growth models (CGM) for predicting the timing and rate of N fertilizer application; this has not been done to date primarily due to complex user-interfaces and a plethora of model inputs. Some commercially available software packages use CGM in back-end, but subscription fees and lack of data-transparency influence adoption. Furthermore, they are difficult to integrate different (or actual) management practices or crops, and some fail to consider actual past weather. These critical gaps will be overcome by using the CGM (APSIM) with Shiny R to create a free and open-source decision-support tool (DST), Optimum Performance Tool for Informed Corn Nitrogen Recommendation - Open-Source(OPTICORN-OS) to stakeholders and can enforce data transparency.

The primary objective of this project is to increase the adoption of DST in agriculture to make an informed decision and following adaptive management in-season to achieve economic, environmental, and social sustainability. We will achieve this by (i) comparing APSIM simulations with field experiments by varying N rates and timings (ii) developing a DST (OPTICORN-OS), based on APSIM, to recommend N-fertilizer application rate and timing by integrating management, soil, and weather information, (iii) conducting user-testing workshops for farmers, extension educators, researchers for using OPTICORN-OS, to improve usability and effectiveness of the tool, and (iv) training farmers, extension educators, and students to use the tool via workshops and extension events. I will work with faculty at Purdue University, with expertise in agronomy, data modeling and simulation, extension, and software development. A field-scale study will be conducted in-parallel with the APSIM simulations. From this research, I will publish two articles and one extension article and present in extension and academic conferences. Workshops will be hosted in the OATSCON-24, Purdue’s Digital Agriculture Showcase, and IoT4Ag annual meeting.

Learning outcomes:

• Farmers and extension educators would be able to better understand the economic and environmental consequences of their in-season decision related to N fertilizer application. These consequences will be estimated through the yield, biomass, amount of N-leaching, and nitrous oxide emissions and will be further validated through the field experiment running parallel to the simulation study.
• We will be developing a decision support tool named Optimum Performance Tool for Informed Corn Nitrogen Recommendation - Open-Source (OPTICORN-OS), to be used to recommend optimum date and rate of N fertilizer application for their fields. Barriers to farmer adoption of the DST will be increased since the tool is free and open-source and prioritizes transparency and usability in design.
• We will conduct surveys on the usability and effectiveness of the decision support tool with farmers, extension educators, researchers, and industry representatives.

Action outcomes

• Farmers and extension educators from Indiana and the broader mid-west, can use this tool to optimize the N-fertilizer application in corn fields.

Condition outcomes

• It improves the economic well-being of farmers, since nitrogen fertilizer application is a high-cost input for corn production.
• Addresses the issue of long-term environmental sustainability because overapplication of N fertilizer, reduces soil health and cause serious environmental consequences.