Optimization of Standard Depth Control Systems to Improve Row-Crop Planter Performance in the Southeast US.
Type of DegreePhD Dissertation
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Modern row-crop planters are designed to place individual seeds in the ground at a proper and predetermined depth to promote immediate germination and uniform emergence of seedlings. Seeding depth is manually adjusted by the operator prior to planting operation by selecting a row-unit depth and a row-unit downforce. Once set, row-unit depth and downforce are not adjusted again for a field though soil conditions may vary, and optimum planting performance requires adjusting planter settings selection to these changing soil conditions. However, limited technology is available today to manage in-field seeding depth variability, and research must be conducted to gain a better understanding of seeding depth and crop response to row-unit depth and downforce adjustments and field spatial variability. The objective of this study was to characterize seeding depth response to changing soil conditions within fields and determine protocol to use active seeding depth by downforce planting technologies to manage in-field seeding variability in the Southeast US. This study was conducted in 2014 and 2015 in Central Alabama for non-irrigated corn (Zea mays L) and cotton (Gossypium hirsutum L). Planting operation was performed using a 6-row John Deere MaxEmerge Plus planter equipped with mechanical heavy duty downforce springs. Three row-unit depths were used along with three row-unit downforce for each crop. Two fields exhibiting typical Coastal Plain features but characterized by different soil properties and terrain attributes were also selected for this study. The experiment was a split-plot design. Soil electrical conductivity (EC) and soil water content at planting were used to describe field spatial variability. Gauge-wheel load was measured in real-time during planting operation at a sampling frequency of 20 Hz. Seeding depth was measured after emergence. Data were analyzed using mixed-effect analyses of variance, linear and polynomial regressions, and spatial methods. Corn and cotton seeding depth increased with row-unit depth and downforce. Row-unit downforce adjustments affected measured seeding depth by as much as 1.1 cm. Corn and cotton seeding depth was significantly affected by changing soil conditions between fields and growing seasons. Shallower seeding depths were achieved in clayier and wetter soil conditions. Measured gauge-wheel load increased with increasing row-unit downforce and reduced with increasing row-unit depth. Significant site-specific seeding depth variability was identified within individual corn trials, and within 1 of 4 cotton trials. Corn seeding depth was not significantly correlated to soil water content within fields. Corn seeding depth was significantly correlated to in-field changes in soil EC. Seeding depth relationship to soil EC explained in-field variations in seeding depth ranging from 0.3 cm to 1.6 cm across individual corn trials. Corn seeding depth was also significantly affected by gauge-wheel load at planting. Corn emergence and yields were primarily affected by changing conditions between fields and growing seasons. Warmer soil temperatures and less clayey soils provided better field conditions for corn emergence resulting in higher final live populations, increased seedling vigor, and quicker and more uniform emergence. Corn emergence was also significantly affected by measured seeding depth and measured gauge-wheel load. Optimum seeding depths and gauge-wheel load optimizing corn emergence were identified for individual field trials. Optimum seeding depths maximizing corn yields were also indentified within 3 of 4 field trials. Improved emergence was correlated to higher yields at harvest. Seeding depth correlation to soil EC within individual field trials enabled to generate prescription maps which could be use to implement prescription-based seeding depth by downforce planting technologies. Equations were developped to describe in-field row-unit depth and downforce adjustments between management zones. Furthermore, results demonstrated the possibility of computing local gauge-wheel load predictions and equations were also developped to describe in-field row-unit depth and downforce adjustments using real-time gauge-wheel load data. Therefore, there is a potential for using site-specific planting technologies to improve seeding depth performance of standard row-crop planters. These technologies could operate based on prescription maps for seeding depth or real-time monitoring of row-unit performance, and more particularly gauge-wheel operation. Most seeding depth adjustments could be provided by dynamic downforce systems, but optimum row-crop planter performance requires joined row-unit depth by downforce adjustments.