Substantiation of the Spatial Orientation of Sunflower Stalks to Enhance the Performance of Roller-Crimpers

Abstract

Modern crop production trends, driven by global climate change and regular moisture deficits, require continuous optimization of tillage and seeding technologies. Large-fraction crop residues of thick-stalked crops, such as sunflower or corn, often obstruct seeding machine components, leading to uneven seed placement depth and a decrease in field germination. Transitioning to resource-saving technologies like No-till or Strip-till is restricted by high amounts of post-harvest residues on the field surface, which demand intensive chopping. Non-driven rotary chopping rollers with knife drums are widely applied due to their simple design, high productivity, and fuel efficiency. However, a significant drawback of existing designs remains the intensive clogging of the inter-blade space with dense soil-plant residue mass, which restricts tool performance.

This research aims to theoretically and experimentally justify the prerequisites for improving the operational efficiency of chopping rollers by establishing mathematical regularities of sunflower stalks’ spatial layout and orientation angles. Field studies were conducted to determine the distribution of sunflower stalks on the field surface after combine harvester passage. The deviation angle of fallen stalks in the horizontal plane relative to the row axis was measured, and conditions of their contact with chopping blades were evaluated. The results indicated that 89% of the stalks lie within a narrow angular range of –5.5° to 5.5°.

Furthermore, laboratory experiments simulated the chopping process under controlled soil hardness and plant moisture conditions using a customized experimental rig. The study analyzed the inter-blade clearance angle (), stalk orientation angle (), soil hardness (), and plant moisture () to determine their impact on clogging probability (). The data proved that increasing the inter-blade clearance angle and the stalk inclination angle significantly reduces clogging. Conversely, decreased soil hardness deteriorates the cutting process, increasing clogging risks, whereas lower plant moisture sharply improves operational reliability. Using mathematical statistics, optimized regression models were developed to predict clogging probability. The derived empirical equations serve as an effective tool for optimizing chopping drum parameters during the initial project engineering stages.