Abstract Soybean Glycine max (L.) Merr. seeding rate is typically set uniformly across entire fields, overlooking within‐field variability. On‐farm experimentation (OFE), combined with precision agriculture (PA) tools, can help determine optimal seeding rates across different areas of a field, generating data that support variable seeding rate decisions. Soil characteristics are factors that can help understand spatial variability in soybean yield and profit, assisting in identifying fields where variable rate seeding is suitable. This study aimed to (i) determine the economically optimal seeding rates per field and per soil type using OFE and (ii) benchmark current farmer seeding rates against the economically optimal seeding rates per field and across soil types. Eleven on‐farm soybean seeding rate trials were conducted in Nebraska, using variable‐rate prescriptions and randomized block designs. Yield data were cleaned and analyzed using mixed models with lme4 package in R to determine the economically optimum seeding rate (EOSR) by field and soil type level and to evaluate seeding rate effects on partial profit. The EOSRs across fields varied from 175,000 to 350,000 ha −1 and could be reduced in nine out of 11 fields without impacting yield and partial profit. Similarly, EOSRs for soil types ranged from 175,000 to 351,000 seeds ha −1 , with significant variation among soil types. This study expands the understanding of within‐field variability in soybean seeding rates based on soil characteristics and highlights the opportunity to reduce seeding costs to optimize partial profit in farmers’ fields.
Betta et al. (Thu,) studied this question.
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