Skip to main content
ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #389477

Research Project: Sustainable Intensification of Cropping Systems on Spatially Variable Landscapes and Soils

Location: Cropping Systems and Water Quality Research

Title: Modeling corn emergence uniformity with on-the-Go furrow sensing technology

item CONWAY, LANCE - University Of Missouri
item Kitchen, Newell
item Sudduth, Kenneth - Ken
item VONG, CHIN NEE - University Of Missouri

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 11/1/2021
Publication Date: 11/7/2021
Citation: Conway, L.S., Kitchen, N.R., Sudduth, K.A., Vong, C. 2021. Modeling corn emergence uniformity with on-the-Go furrow sensing technology [abstract]. ASA-CSSA-SSSA Annual International Conference, November 7-10, 2021, Salt Lake City, Utah. Available:

Interpretive Summary:

Technical Abstract: Integration of proximal soil sensors into row-crop seeding equipment has allowed for a dense quantification of spatial variability. Output from the sensors can be used to automate real-time adjustments to key planter row-unit functions, such as seeding depth or rate. However, research is needed to determine whether sensor-driven automation can consistently be used to optimize row-crop emergence uniformity. Therefore, a study was conducted to evaluate the ability of row-unit mounted sensors to estimate corn emergence uniformity. Field research was conducted in 2020 and 2021 in central Missouri, USA. Soil sensor data were collected during corn seeding with Precision Planting’s SmartFirmers and DeltaForce systems. Output from these systems included multiple sensor-data layers, such as soil organic matter, furrow moisture, and row-unit downforce. Additionally, imagery from an unmanned aerial vehicle (UAV) were collected around the second vegetative growth stage. Established methods were used to estimate early stand and emergence uniformity across the entire field area using the UAV imagery. All planter data layers were then used as input in a statistical learning model to estimate early stand count and emergence uniformity. Results from 2020 showed that the systems could account for 40% of the variation in early stand count, with organic matter explaining the greatest amount of variability. Early stand uniformity estimation was less successful, where the systems were only able to capture 20% of the variation. Collectively, results show these technologies give an estimate of early crop emergence performance, but that additional input is required for consistent implementation.