Adjusting soybean planting date, maturity group, and seeding rate will be critical to raise North Carolina soybean yield. Foundational research was conducted on these management practices in this state from 2019-2021 (11 sites of useable data) to generate a baseline dataset that focused on maximizing yield under our diverse production scenarios. What is now needed is additional years of data that can capture added variability in annual weather patterns, soil types, regions, and latitudes to generate a robust enough dataset to create a grower decision support tool as the final product of this field research. The ultimate goal is to generate 15-20 useable sites of data that will be the base to create this grower decision support tool that will allow NC soybean producers to predict optimum production practices across planting dates under a range of scenarios (region, soil type, latitude, weather pattern, etc.). Beyond the focus of maximizing yield, data will also be collected to understand management implications on soybean seed quality (protein, oil, damage) in the state and we will work with the Risk Management Agency (RMA) to provide the needed data to consider adjusting the soybean insurance coverage periods in North Carolina.

1. Generate robust information for North Carolina producers on ideal planting date for soybeans to maximize yield and simultaneously generate the data needed for the Risk Management Agency to adjust the dates of insurance coverage for soybeans in North Carolina.
2. Produce the data needed for recommendations on modifying soybean maturity group and population across the wide range of planting dates used in North Carolina.
3. Capture seed quality (protein, oil, damage) data across planting date and maturity group combinations in an effort to understand the implications of adjusting these production practices on soybean seed quality. Additionally, more deeply explore the relationship between yield and soybean quality.
4. Use the generated field data to develop a grower decision support tool that will allow growers to optimize production across planting date in a collaboration with NC State Extension IT.
5. Extend results to County Extension Agents, soybean producers, and other soybean stakeholders throughout North Carolina.

Expected end products include, but are not limited to:
Grower decision support tool
Adjusted soybean replant insurance coverage dates
Grower meeting presentations
Field day presentations
High quality videos
Electronic media updates
Social Media
Webinars
Peer-reviewed extension publications
Publication in a high-impact journal (target: Agronomy Journal, Crop Science)

A recent analysis of 877 entries into the North Carolina Soybean Yield Contest over an 18-year period indicated that planting date and maturity group were two of the strongest predictors of high soybean yield in the state. Despite this, North Carolina soybean producers continue to plant soybeans across a wide range of planting dates for a variety of reasons. Growers need the data that consistently demonstrates annual implications of using various planting dates to incentivize earlier planting and to understand how production practices should be adjusted across planting date. The dataset aimed to help growers with these management decisions has been being built over the past three years (2019-2021) using small-plot research. What has become evident is that the annual variability in weather in North Carolina makes it challenging to make strong recommendations about adjusting production practices based on 2-3 years of data.

What is now needed is additional years of data that can capture added variability in annual weather patterns, soil types, regions, and latitudes to generate a robust enough dataset to create a grower decision support tool as a product of this field research. This tool can predict the performance of various planting dates and maturity groups across a range of production scenarios. As producers adjust these production practices, it will also be important to understand the implications of soybean seed quality, and that data will be generated from this project.