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Application of an AgLeader® Cotton Yield Monitor for Measuring Peanut Yield: A Multi-State Investigation
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: Paper number 131596295, 2013 Kansas City, Missouri, July 21 - July 24, 2013. (doi: http://dx.doi.org/10.13031/aim.20131596295) @2013
Authors: Wesley M. Porter, Jason Ward, Kendall R. Kirk, Randal K. Taylor, Jacob B. Fravel, Chad B. Godsey
Keywords: Peanut Yield Monitor Moisture Content Optical AgLeader
Abstract. Previous researchers demonstrated the ability to adapt an AgLeaderÂ® Cotton Monitor to a peanut combine. It was shown that the field weight could be accurately predicted with errors less than 10%. This project focuses on expanding this previous work and the work by Porter et al. (2012) by incorporating a protective deflector plate for the sensors, obtaining multiple field weights, and using the peanut sale sheets to correlate yield monitor yield to sale weight. This study was a multi-university, multi-state effort, including Oklahoma State University (Oklahoma), Mississippi State University (Mississippi) and Clemson University (South Carolina). The numbers and other parameters reported in the peanut grade sheet will be used to explain errors and aid in the calibration process of the optical monitor. Data collected during this study includes multiple loads which include yield monitor weight, field weight, field moisture content, and all the information present on peanut grade sheets, when available. The multi-state effort allowed for the incorporation of the three major peanut types including Runner, Spanish, and Virginia, and for the incorporation of different soil types. The goal of this study is to develop guidelines for using and developing this cotton yield monitor for peanut harvest. Since the seller gets paid based on the dry clean weight of peanuts a monitor that is reporting only the actual field yield is not of much value except for spotting yield trends, but not for aiding in predicting actual sale yield accounting for trash and moisture content.
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