Author
HARRIGAN, T - MICHIGAN STATE UNIVERSITY | |
Rotz, Clarence - Al |
Submitted to: Applied Engineering in Agriculture
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 7/10/1995 Publication Date: N/A Citation: N/A Interpretive Summary: Draft information is frequently used in machinery management. Farm managers and consultants use draft or power data to match tractors with implements and to estimate fuel requirements. For forty years, ASAE has published draft and power data for agricultural equipment as a standard for use by machinery managers. Periodic updating of this data is required to maintain current and useful information. The last revision of the draft data was done over fifteen years ago, and many changes in tillage and planting equipment have occurred since that time. Conservation tillage practices have largely replaced moldboard plowing and conventional seedbed tillage. Tillage tools that allow a range of control over the amount of crop residue left on the soil surface, and combination tools that combine multiple tillage operations are now in common use. A general model with machine and soil specific parameters was developed to update the ASAE Standard data. This new model provides machinery managers a more accurate and current procedure for estimating power requirements. Technical Abstract: Farm managers and consultants use draft or power data to match tractors with implements and to estimate fuel requirements. A general model with machine specific parameters is proposed to predict the average draft of tillage and seeding implements under general conditions. Implement draft is modeled as a function of soil texture, implement width, tilled depth and operating speed for most major operations. The model requires little knowledge of specific soil conditions. Machine and soil specific parameters are presented in a convenient reference table. The model and reference table are to be considered in the revision of ASAE Standards EP496, Agricultural machinery management and D497 Agricultural machinery management data. |