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Field Evaluation of a Rising Plate Meter to Estimate Herbage Mass in Austrian Pastures
Field Evaluation of a Rising Plate Meter to Estimate Herbage Mass in Austrian Pastures
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Field Evaluation of a Rising Plate Meter to Estimate Herbage Mass in Austrian Pastures
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Field Evaluation of a Rising Plate Meter to Estimate Herbage Mass in Austrian Pastures
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Field Evaluation of a Rising Plate Meter to Estimate Herbage Mass in Austrian Pastures
Field Evaluation of a Rising Plate Meter to Estimate Herbage Mass in Austrian Pastures
Journal Article

Field Evaluation of a Rising Plate Meter to Estimate Herbage Mass in Austrian Pastures

2023
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Overview
Pasture management is an important topic for dairy farms with grazing systems. Herbage mass (HM) is a key measure, and estimations of HM content in pastures allow for informed decisions in pasture management. A common method of estimating the HM content in pastures requires manually collected grass samples, which are subjected to laboratory analysis to determine the dry matter (DM) content. However, in recent years, new methods have emerged that generate digital data and aim to expedite, facilitate and improve the measurement of HM. This study aimed to evaluate the accuracy of a rising plate meter (RPM) tool in a practical setting to estimate HM in Austrian pastures. With this study, we also attempted to answer whether the tool is ready for use by farmers with its default settings. This study was conducted on the teaching and research farm of the University of Veterinary Medicine in Vienna, Austria. Data were collected from May to October 2021 in five different pastures. To evaluate the accuracy of the RPM tool, grass samples were collected and dried in an oven to extract their DM and calculate the HM. The HM obtained from the grass samples was used as the gold standard for this study. In total, 3796 RPM measurements and 203 grass samples yielding 49 measurement points were used for the evaluation of the RPM tool. Despite the differences in pasture composition, the averaged HM from the RPM tool showed a strong correlation with the gold standard (R2 = 0.73, rp = 0.86, RMSE = 517.86, CV = 33.67%). However, the results may not be good enough to justify the use of the tool, because simulations in economic studies suggest that the error of prediction should be lower than 15%. Furthermore, in some pastures, the RPM obtained poor results, indicating an additional need for pasture-specific calibrations, which complicates the use of the RPM tool.