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ARS Home » Northeast Area » University Park, Pennsylvania » Pasture Systems & Watershed Management Research » Research » Publications at this Location » Publication #331926

Research Project: Multifunctional Farms and Landscapes to Enhance Ecosystem Services

Location: Pasture Systems & Watershed Management Research

Title: Evaluation of a rising plate meter for use in multi-species swards

item Dillard, Sandra
item HAFLA, AIMEE - Agri-King, Inc
item Rubano, Melissa
item Stout, Robert
item BRITO, ANDRE - University Of New Hampshire
item Soder, Kathy

Submitted to: Agricultural and Environmental Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/20/2016
Publication Date: 11/10/2016
Citation: Dillard, S.L., Hafla, A.N., Rubano, M.D., Stout, R.C., Brito, A.F., Soder, K.J. 2016. Evaluation of a rising plate meter for use in multi-species swards. Agricultural and Environmental Letters. 1:160032.

Interpretive Summary: The rising plate meter is an important tool for measuring grass yields. This study by ARS scientists at University Park, PA looked across different methods to improve the accuracy and identified best practices to ensure minimum errors.

Technical Abstract: The rising plate meter (RPM) provides rapid estimates of herbage The rising plate meter (RPM) provides rapid estimates of herbage mass (HM). Accurate 19 calibration of the RPM is difficult due to variability in forage management, growth and species 20 composition. The RPM is typically calibrated by linear regression of HM and RPM height; 21 however, the r2 is usually low. Curvilinear regression, with the X-intercept set to zero, could 22 provide a more robust calibration equation and decrease variability in RPM estimates. Three 23 Pennsylvania dairy farms grazing lactating dairy cattle on multi-species pastures were used to 24 determine HM and RPM height using 3 transects per pasture. Removal of the X-intercept 25 increased the adjusted-r2 by 42.8 to 89.0%. Use of quadratic and cubic regression only resulted in 26 0.01 to 0.02 increase in adjusted-r2. Linear regression remains the simplest and preferred method 27 of calibration; however, error can be reduced by setting calibration equations so that 0 RPM 28 height is associated with zero HM.