Corn sensor-based variable rate N management

Published

August 4, 2022

Background

Nitrogen (N) is the most common limiting nutrient to optimum corn yields. Within-field variability in soil characteristics and properties like elevation, slope, texture, bulk density, organic matter, nutrient concentration, and pH can create variable N availability, corn growth, and yield potential.

The detection of within-field corn growth variability and its proper management can be performed using crop canopy sensors. These sensors vary distance to the plants and in sensor-related resolutions (spatial, temporal, spectral, and radiometric). Commonly used crop canopy sensors include proximal (<1 m from plants) and remote (ranging from 50 m with UAVs to orbital distances with satellite). Proximal and remote sensors have their own advantages and disadvantages, with proximal sensors generally being more sensitive to crop growth differences but less scalable than remote sensors.

The use of in-season crop canopy sensors to variably apply N in corn has been intensively studied in the US Midwest, Brazil, Argentina, and Australia corn growing regions. However, limited studies have been conducted in the US Southeast region with its particular warmer climates and sandier soils.

Therefore, more studies are needed to understand the potential of variable rate N management to maintain and improve corn yield, N use efficiency, and profitability in the challenging environments of the US Southeast region.

Hypothesis and Objectives

We hypothesize that

  • the use of proximal, UAV, and satellite sensors will produce similar in-season variable rate N rates in corn,

  • sensor-based N management will yield at least similarly to a flat-rate approach,

  • sensor-based N management will be more efficient in the use of N compared to a flat-rate approach.

The objectives of this study are to assess

  1. how different are N rates when calculated using proximal, UAV, and satellite data,
  2. the effect of sensor-based nutrient management in corn yield and N use efficiency

Timeline

This study will start in the Spring of 2023. A more detailed timeline will be created in the future.