Location: Cropping Systems and Water Quality Research
Project Number: 5070-21600-001-014-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jun 1, 2025
End Date: Dec 31, 2026
Objective:
To understand and manage spatial nitrogen fertilizer response across farmer's fields in response to varying farm management (e.g., cover crops, irrigation, fertilizer type and placement) in central Missouri. Specific objectives are to:
1) utilize farmers' precision-agriculture technology to vary nitrogen rates as "plots" across an entire field.
2) collect soil samples to evaluate which soil physical, chemical, and biological measurements best relate to corn nitrogen response.
3) collect additional data layers (e.g., Digital Elevation Model (DEM), unmanned aerial vehicle (UAV) imagery, electrical conductivity (ECa), historical yield) to identify which layers are the most helpful at identifying spatial corn yield response to nitrogen.
4) Gather spatial biomass estimates of cover crops for later analysis.
5) develop new nitrogen recommendation tools for Central Missouri based on these findings.
Approach:
A minimum of 10 nitrogen (N) rate trials will be conducted across central Missouri during 2025 and 2026. Each of these trials will include at least five N fertilizer rate treatments replicated across an area of 30 acres or more. The trial design will be developed in collaboration with participating farmers based on their equipment and current nitrogen fertilizer management plans (fertilizer type, rates, placement, and timing). Soil sampling will occur prior to planting for soil health and fertility analysis. Spatial measurements of grain yield, grain N content, estimates of cover crop biomass, soil health, and soil fertility will be used to contrast differences in corn grain response to added fertilizer N. Additionally, these measurements will be used as covariates to determine optimal N application rates for grain yield and optimizing N fertilizer for maximum profitability. ARS will participate in all objectives by assisting with the designing of experiments, providing guidance on 1) protocols for collecting and analyzing soil samples, 2) soil surveying, and 3) assisting with data analysis (i.e., feature cleaning, running machine learning models, feature selection, and interpretation of results). For objective 4 (estimating cover crop biomass), geolocated biomass samples (e.g., total wet and dry weight of 1 m^2 quadrat of aboveground tissue) will be taken (n=15) in different areas of the field to represent low, medium, and high biomass growth. Additional methods will be tested to obtain estimated cover crop biomass across the entire field. These methods include relating remote sensing (UAV-Red, Green, and Blue or UAV-LiDAR) data to the geolocated biomass samples using machine learning approaches to obtain a field-scale estimate of cover crop biomass. This data layer will be used to determine if corn nitrogen rates need to be adjusted based on cover crop biomass and type.