It is widely recognized that planting soybean [Glycine max (L.) Merr.] early is critical to maximizing yield, but the influence of changing management factors when soybean planting is delayed is not well understood. The objectives of this research were to (a) identify management decisions that increase seed yield in either early- or late-planted soybean scenarios, and (b) estimate the maximum break-even price of each management factor identified to influence soybean seed yield in early- or late-planted soybean. Producer data on seed yield and management decisions were collected from 5682 fields planted with soybean during 2014−2016 and grouped into 10 technology extrapolation domains (TEDs) based on growing environment. A subsample of 1512 fields was classified into early- and late-planted categories using terciles. Conditional inference trees were created for each TED to evaluate the effect of management decisions within the two planting date timeframes on seed yield. Management strategies that maximized yield and associated maximum break-even prices varied across TEDs and planting date. For early-planted fields, higher yields were associated with artificial drainage, insecticide seed treatment, and lower seeding rates. For late-planted fields, herbicide application timing and tillage intensity were related to higher yields. There was no individual management decision that consistently increased seed yield across all TEDs.
Bibliographical noteFunding Information:
We acknowledge the North-Central Soybean Research Program (NCSRP), Nebraska Soybean Board, and Wisconsin Soybean Marketing Board for their support to this project. We also thank UNL Extension Educators, Nebraska Natural Resource Districts, OSU Extension Educators, and Iowa Soybean Association for helping collect the producer data; and Doug Alt for helping gather input price data. Finally, we thank Lim Davy, Agustina Diale, Juan Pedro Erasun, Laurie Gerber, Clare Gietzel, Mariano Hernandez, Ngu Kah Hui, Caleb Novak, Juliana de Oliveira Hello, Pedro Rocha Pereira, Matt Richmond, and Paige Wacker for help inputting and cleaning the survey data.