Clinical Unit for Herd Health Management in Ruminants, University Clinic for Ruminants, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, 1210 Vienna, Austria.
Unit for Reproduction Medicine and Udder Health, Clinic for Farm Animals, Freie Universitaet Berlin, 14163 Berlin, Germany.
Sensors (Basel). 2023 Aug 28;23(17):7477. doi: 10.3390/s23177477.
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 (R = 0.73, r = 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.
牧场管理是放牧系统奶牛场的一个重要课题。牧草量(HM)是一个关键的衡量标准,对牧场中 HM 含量的估计可以为牧场管理做出明智的决策。一种常见的估计牧场 HM 含量的方法需要手动采集草样,然后在实验室进行分析以确定干物质(DM)含量。然而,近年来,出现了一些新的方法,可以生成数字数据,并旨在加快、便利和改进 HM 的测量。本研究旨在评估在实际环境中使用升板仪(RPM)工具来估计奥地利牧场 HM 的准确性。通过本研究,我们还试图回答该工具是否可以在不进行任何修改的情况下供农民使用。本研究在奥地利维也纳兽医大学的教学和研究农场进行。数据于 2021 年 5 月至 10 月在五个不同的牧场采集。为了评估 RPM 工具的准确性,采集了草样并在烤箱中烘干以提取其 DM 并计算 HM。本研究中使用从草样中获得的 HM 作为金标准。共使用 3796 次 RPM 测量和 203 个草样产生 49 个测量点来评估 RPM 工具。尽管牧场组成存在差异,但 RPM 工具获得的平均 HM 与金标准具有很强的相关性(R = 0.73,r = 0.86,RMSE = 517.86,CV = 33.67%)。然而,结果可能还不够好,无法证明该工具的使用是合理的,因为经济研究中的模拟表明,预测误差应该低于 15%。此外,在一些牧场中,RPM 得到的结果较差,这表明需要进行特定于牧场的校准,这使得 RPM 工具的使用变得更加复杂。