Modeling the Effect of Varied and Fixed Seeding Rates at a Small‐Plot Scale

Core Ideas Quadratic and quadratic‐plateau responses were most common in Ohio and Illinois, respectively. Optimizing seeding rates in Ohio resulted in greater return to seed than in Illinois. Factors other than yield potential contribute toward yield response to seeding rate. The optimum corn (Zea mays L.) seeding rate which maximizes profitability can vary with small differences in environmental characteristics. Because environmental characteristics can vary at small spatial scales, the optimum seeding rate might vary similarly. The objectives of this study were to use data from 125 small‐plot field trials in Ohio (93) and Illinois (32) to model the effects of varied and fixed seeding rates on corn yield production, return to seed (RTS), and determine if optimal seeding rate changed with yield potential. The mathematical relationship between yield and seeding rate was used to determine the economic optimum seeding rate (EOSR) for each trial (assuming a seed cost of US$3.00 per thousand seeds and grain price of .148 kg−1) and model the uniform optimum seeding rate (UOSR) for each state that would result in greatest overall profitability if used in every trial. Estimated RTS was determined by subtracting the averaged value from implementing the UOSR in every trial from the averaged EOSRs from each trial. The EOSR (varied seeding rate) provided greater RTS when compared to the UOSR (fixed seeding rate) in each state, but the potential was greater in Ohio ($30.98 ha−1) than in Illinois ($7.76 ha−1). However, relatively flat population curves suggest that deviation from the optimum seeding rate may have little consequence for yield (<1.5% loss at the UOSR compared to EOSR). This suggests accurate characterization of responses may be difficult to attain for use in variable rate seeding applications.