Click on “Download PDF” for the PDF version or on the title for the HTML version. If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options. A Numerical Approach for Evaluating and Properly Setting Self-Propelled Forage HarvestersPublished by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org Citation: Transactions of the ASABE. 56(1): 5-14. (doi: 10.13031/2013.42580) @2013Authors: M. Bietresato, S. Pavan, G. Cozzi, L. Sartori Keywords: Corn silage, Multilinear regression model, Response surface modeling, Self-propelled forage harvester, Theoretical cut length The choice and setup of self-propelled forage harvesters (FHs) in the cost-effective production of quality silage must consider both energetic and zootechnical issues. A global evaluation of whole-plant harvesting operations includes an assessment of the fundamental in-field performance of the machines (theoretical field time and fuel consumption) and the physical and nutritional traits of the corn silage (particle size, dimensional fractions, and level of kernel breakage). However, finding the optimal settings for a machine while taking into account the variability of the field is difficult and experimentally expensive, as there are many factors that can influence these parameters and whose action is not yet completely known. All of these parameters were investigated through an experimental plan including different adjustments of the theoretical cut length (TCL; 10 to 20 mm), processor roller clearance (PRC), and roller speed difference (13%, 25%, and 60%) of the corn conditioner device (CCD) on three different models of self-propelled forage harvesters (425 to 606 kW engine power) with two headers (6.0 and 7.5 m), used for harvesting whole-plant corn silage at different sites (crop yield of 37.12 to 66.96 t ha-1, and dry matter content of 35.93% to 44.52%). The collected data were then processed by performing several statistical analyses (ANOVA), and the coefficients of the regression models (response surface modeling, RSM) were calculated. From this analysis, it was evident which factors influenced the analyzed responses, and several numerical models, approximating the real behavior of the machines within the ranges of the tested factors, were generated. The aim of this work was to demonstrate the effectiveness of this approach in investigating the relationships among these variables to evaluate and properly set up the machines. The results revealed some general correlations among variables and responses, particularly with regard to the quality of the final product, and they provided some basic criteria for refining the settings of the cutterhead of the forage harvesters, particularly the TCL, and of the crop processor, specifically the clearance and speed difference of the rollers. (Download PDF) (Export to EndNotes)
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