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Soybean seeding rate (SR) is selected according to planting date, maturity group, soil properties, and yield goal. Even though one SR is applied in most fields, site-specific adjustments with variable-rate seeding (VRS) technology can help optimize production. The project goal was to determine if VRS could be beneficial to soybean producers in Arkansas. The project objectives were to i) assess planter performance and soybean yield response to five seeding rate treatments, ii) characterize the drivers of yield variability, and iii) create a-posteriori prescription maps for VRS. Five SR treatments (185,000, 247,000, 309,000, 370,000 and 432,000 seeds ha-1) were applied using a randomized complete block strip design in two site-years referred to as production fields A (2021) and B (2022) in Lincoln County in Arkansas. As-applied SR were collected from the planter. Plant population was calculated from stand counts collected at the four to five trifoliate stages. Yield monitor data were collected at harvest. As-applied SR, plant population, and yield data were averaged by strip and analyzed using mixed-effect models. While significant differences in as-applied SR and plant population (P=0.05) were observed between treatments, there was no difference in yield. Yet, the within treatment yield variability calculated as a coefficient of variation ranged from 4% to 14% and further analysis was computed to identify the drivers of in-field variability. Soil mapping unit (SMU) information was downloaded from the Soil Survey Geographic database. Soil samples were collected in 91 and 80 locations in fields A and B to characterize soil pH, potassium (K), phosphorus (P), and soil texture defined by both the textural class and percent sand, silt, and clay. Digital elevation models (DEM) were downloaded from the United States Geological Survey public data repository and used to compute flow accumulation. A total of 3,586 and 2,153 grid points were created in field A and B. Soil texture, pH, K, and P, SMU, elevation, and flow accumulation were estimated in each grid point using interpolation or extraction. A 100-fold cross-validation with a 10% calibration/90%validation data split was computed to identify the model that best describes site-specific relationships between yield, SR, and soil properties in each field. The best model described yield as a function of the interaction between SR and both soil pH and soil K in field A. The best model described yield as a function of SMU, percent sand – both linear and quadratic effects-, percent clay, and the interaction between SR and percent clay in field B. As the interaction between SR and other parameters were significant in both fields, a-posteriori prescription maps were created using the following approach. Yield was predicted for each SR treatment and grid point using the best models. Grid points were organized in groups of 4 to create 896 and 538 management zones (MZ) in fields A and B, respectively. Analysis of variance and post-hoc analyses were computed to identify the optimum SR in each MZ. Results were summarized into prescription maps. Future research may include comparison of results between growing seasons, economic analysis, implementation, and on-farm validation.

Soybean [Glycine max (L.) Merr] inputs are continually increasing, and with market values decreasing, producers are forced to find ways to maintain profitability. To address these challenges, soybean producers are interested in reducing seeding rates. However, due to within field variability, it may not be possible to lower seeding rates throughout an entire field uniformly and still achieve the same soybean yield. Variable rate seeding (VRS) of soybean allows producers to adjust seeding rates according to the variability in their fields. However, little is known regarding the accuracy of farmers’ VRS prescriptions. The objectives of this research were to 1) determine the agronomic optimum seeding rate (AOSR) and the economic optimum seeding rate (EOSR) in predetermined management zones, 2) compare the calculated AOSR and EOSR to each producer’s VRS prescription, 3) determine the impact of final plant stand on yield within management zones, 4) identify how soybean plant architecture maintains yield across multiple seeding rates, and 5) determine how plant population impacts harvest, especially combine fuel use and harvest grain loss. In 2017 and 2018, eight on-farm trials were conducted across Ohio. The trials consisted of three uniform seeding rates of 247,000, 346,000, 445,000 seeds ha-1, and a variable rate strip determined by the producers ranging from 198,000 to 445,000 seeds ha-1. The AOSR (yield maximizing) and EOSR (profit maximizing) were calculated from regression analyses for each management zone and field. Agronomic and economic optimum seeding rates ranged from 445,000 seeds ha-1 depending on the site-year. Final plant stands varied across site- years, but the calculated agronomic optimum final stand (AOFS) were similar to the recommended AOFS of 247,000 to 297,000 plants ha-1. At lower final stands, soybean yield was maintained by the plants’ ability to grow lateral branches that produced pods. In 2017 and 2018, two management zones were seeded at the calculated AOSR and one zone was seeded at the EOSR, indicating that the assigned seeding rates were not effective for these management zones. Combine fuel use and grain loss at harvest were also analyzed using regression analysis. Soybean grain yield had the largest impact on combine fuel use, increasing as grain yield increased. Plant moisture, grain moisture, branching, and stem hardness did not impact combine fuel use. When final plant populations were reduced, the pods that were closer to the ground were difficult to harvest, which led to increased harvest loss in soybean populations