Planters with mounted proximal soil sensing systems can densely quantify seed zone soil variability. Technology now allows for real-time sensor information to control multiple row-unit functions on-the-go (e.g., planting depth). These and other developing sensor-based control systems have the potential to greatly improve correctness when planting, and therefore row-crop performance. For sensor-based control to be widely adopted, practitioners must understand the precision and utility of the systems. Therefore, research was conducted to: (i) determine how well commercially available sensors can estimate soil organic matter (OM) and whether sensor output was repeatable among sensing dates; (ii) evaluate OM prediction accuracy across selected soils and soil volumetric water contents with both a commercially-available, planter-mounted sensor, and machine learning techniques applied to multiple combinations of soil reflectance bands within the visible and near infrared spectrum; and (iii) investigate if planter and other proximal soil sensor data, in combination with topographic features, could predict field-scale corn emergence rate at varying planting depths. Results found that commercial sensors could estimate general trends in spatial variability of OM, but that some inconsistencies were associated with a "global" calibration that appeared susceptible to temporal variations in soil water content. In the controlled environment, results for sensor estimation of OM were similar to the field study. Further, results showed that spectral information within the entire range used by the commercial systems evaluated was required to consistently predict OM at varying volumetric water contents. Lastly, the field-scale agronomic analysis found that inherent soil and landscape variability drove the emergence rate response at the site. However, planter metrics were still usefulIncludes bibliographical references
