Project Details:

Site-specific weed management with precision application technology

Parent Project: This is the first year of this project.
Checkoff Organization:North Central Soybean Research Program
Categories:Technology, Weed control, Sustainability
Organization Project Code:
Project Year:2023
Lead Principal Investigator:Chris Proctor (University of Nebraska at Lincoln)
Co-Principal Investigators:
Anita Dille (Kansas State University)
Rodrigo Werle (University of Wisconsin - Madison)
Keywords: Precision-application-technology Targeted-weed-management Camera-weed-detection Herbicide-savings

Contributing Organizations

Funding Institutions

Information and Results

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Project Summary

Herbicides are the primary tool used for weed management in soybean (USDA – National Agricultural Statistics Service). Difficult to manage weeds (pigweed species, ragweed species, foxtail species, common lambsquarters, kochia, horseweeed, etc.) as well as herbicide-resistant populations for many of these weeds have been selected over time across North Central US soybean cropping systems. Increasing herbicide cost and environmental concerns have only added to management challenges. Current herbicide application practices focus on broadcast applications that deliver a constant herbicide rate across an entire field. However, weed populations have great spatial variability that is influenced by factors such as weed species biology, soil type, crop rotation, and tillage and harvest practices. Previous research has evaluated site-specific weed management (SSWM) using weed species distribution maps and site-specific herbicide applications resulting in herbicide savings ranging from 23 to 89% (Gerhards et al., 2022). Despite the potential economic and environmental benefits with SSWM, current technologies have not been able to achieve this at field scale. With recent improvements and integration of active sensors and sprayer technologies, the ability to detect and treat weeds on a real-time site-specific basis should soon become feasible. Several companies are developing smart sprayers with the ability to detect and spray weeds in real-time both in fallow (green on brown) and early-season in crop (green on green). This technology has potential to shift from broadcast herbicide applications that deliver a constant herbicide rate across an entire field to only treating parts of the field where weeds occur. This would improve herbicide efficacy as doses could be adjusted based on weed size and weed species (as detection technology improves for species identification) potentially reducing overall herbicide cost and use. This system has potential to reduce future weed infestation levels using technology driven SSWM.
The long-term goal of this research project is to optimize herbicide applications with a smart sprayer system in the US to stimulate the adoption of precision agriculture tools for more sustainable weed management programs.
Our research team has the skills and research equipment related to weed management and pesticide application technology to successfully complete this project. The research project is planned to be conducted at the University of Wisconsin-Madison (Werle), University of Nebraska-Lincoln (Proctor), Kansas State University (Dille), and the BASF Midwest Research Farm near Seymour, IL (Kruger/Werle). The project collaborators have a well-established partnership with BASF (Dr. Greg Kruger, Dr. Bruno Vieira, et al.) enabling access to a small plot (mobile 10-ft tractor mounted) and two large-scale sprayers (90-ft boom, one located in NE and one in IL) equipped with the BASF and Bosch Smart Spraying Technology. These four research sites are located in strategic soybean producing regions of the United States. Moreover, these research sites are infested with the most common and troublesome weeds that North Central US soybean growers are facing (pigweed species, ragweed species, foxtail species, common lambsquarters, kochia, horseweed, etc.). While the BASF and Bosch Smart Spraying technology will be used in this project, inferences can be drawn for other similar technologies. This project will evaluate different configurations of site-specific herbicide applications to control weed infestations in soybean using POST herbicides. Studies will test the BASF and Bosch Smart Spraying Technology with large-scale (studies with >40 acres) field projects in IL and NE and small plot projects in IL, KS and WI to evaluate weed detection, targeted spray deposition, weed control, end-of season weed seed production, reduction in herbicide usage for spot-spray compared to broadcast herbicide applications, and soybean yield.
Multiple years are planned to be able to follow weed community response to spot-spray applications under a soybean-corn rotation with each phase of the rotation planted and evaluated each year. Plots will be established, maintained and evaluated for multiple years with same treatments applied to each plot through time.
A range of treatments will be evaluated in small- and large-plot studies. The large-plot sprayer is a commercial sprayer with one tank - one boom setup. The small-plot sprayer is designed with two spray tanks and two separate booms. This provides an opportunity to evaluate different application approaches (treatment 1, 1 tank + 1 boom) with a broadcast application, or as a spot spray application (treatment 2, 1 tank + 2 boom). With the two-boom system, a broadcast rate would be applied for control of small or undetected weeds while the second boom, once a weed is detected, will spot-spray that weed with a spiked rate of the same spray solution increasing the dose to the full label rate only where weeds are detected. A 2 tank + 2 boom system will allow for two different mixtures (e.g., soil-applied residual herbicide in one tank and postemergence herbicide in other tank) where the first boom can apply a low- dose of a burndown herbicide and include a residual herbicide as a broadcast application and the second boom can spike the POST herbicide, up to the high label rate, for detected weeds. These different configurations will be evaluated and provide ultimate flexibility in weed control with latest chemistries and sprayer application technologies.

Project Objectives

Main objective is to Evaluate and demonstrate the efficacy of postemergence spot-spray herbicide programs in soybean on weed populations over time using the BASF Digital Farming GmbH (BASF) and Robert Bosch GmbH (Bosch)Smart Sprayer

Project Deliverables

• Technical Extension bulletins will be generated for growers and decision influencers.
• Several young professionals that will soon join the workforce and influence decisions will be trained on this novel technology while supporting the research.
• Field day demonstrations for soybean growers – especially targeted for North Central soybean checkoff members
• Social media posts highlighting in-season equipment demonstrations
• Multiple refereed journal articles, conference presentations, and proceedings will be developed with the project findings.
• Conference papers will be presented at professional meetings (e.g., North Central Weed Science Society, Weed Science Society of America) to discuss the study methods and research findings.
• Manuscripts will be published in scientific journals (e.g., Pest Management Science, Weed Technology).

Progress of Work

Final Project Results

Benefit to Soybean Farmers

This project will:
-Evaluate new weed management smart sprayer technology that may improve control of hard to manage/resistant weeds, reduce herbicide use/cost
-Investigate smart sprayer technology to determine how it might most effectively be utilized for managing weeds in soybean
-Provide insight into the potential return on investment of new smart sprayer technology

Performance Metrics

• Field studies designed and implemented at four locations: IL, KS, NE, WI
• Data collected and visualized (maps, figures, tables)
• Online extension publication website views
• Research publication citations
• Field day survey results
• Soybean grower feedback from field day demonstrations
• Social media responses and feedback (likes, content shared, comments)

Project Years