Abstract The accurate estimation of herbage intake is a key to adequately feed grazing ruminants. Ten dairy Sarda sheep fitted with a halter equipped with an accelerometer (BEHARUM device) were allowed to graze micro-swards of Italian ryegrass (Lolium multiflorum L.), alfalfa (Medicago sativa L.), oat (Avena sativa L.), chicory (Cichorium intibus L.) and a mixture (Italian ryegrass and alfalfa) for six minutes. Accelerometer data and video recordings of behaviour were collected simultaneously. The raw acceleration data were processed to calculate 15 variables: sum, mean, variance and inverse coefficient of variation (ICV, mean/standard deviation) for the X, Y and Z axes and the resultant. A database was created that included the acceleration variables and herbage intake (FMI, DMI, g), intake rate (FMIR, DMIR, g/minute), bite mass (FMBM, DMBM, g) either on fresh (FM) or dry matter basis (DM) and the logarithm of number of bites (LB) and bite rate (LBR) measured during the tests. Partial least square regression analysis (PLSR) was used to verify if acceleration variables could be used as predictors of behavioural traits. The precision and accuracy of PLSR were evaluated implementing the Model Evaluation System, in which predicted values were regressed against observed ones, based on r2 and Dent & Blackie test. The PLSR showed an overall good accuracy (Dent & Blackie test P = ns) and was proven precise for the estimation of LB (r2 = 0.87), LBR (r2 = 0.86), DMI and DMIR (r2 = 0.71). To conclude, BEHARUM can accurately estimate with high to moderate precision number of bites and herbage intake of sheep short term grazing Mediterranean forages.

中文翻译：

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在短期放牧试验中，通过基于加速度计的系统对暴露于不同草料的奶羊的咬合次数和草料摄入量进行预测

摘要 准确估计牧草采食量是给放牧反刍动物充足饲料的关键。十只装有带加速计（BEHARUM 装置）的缰绳的乳制 Sarda 羊被允许放牧意大利黑麦草（Lolium multiflorum L.）、苜蓿（Medicago sativa L.）、燕麦（Avena sativa L.）、菊苣（Cichorium intibus L.）和混合物（意大利黑麦草和苜蓿）6 分钟。同时收集加速度计数据和行为视频记录。处理原始加速度数据以计算 15 个变量：X、Y 和 Z 轴的总和、平均值、方差和逆变异系数（ICV，平均值/标准偏差）及其结果。创建了一个数据库，其中包括加速度变量和牧草摄入量（FMI、DMI、g）、摄入率（FMIR、DMIR、g/分钟）、咬合质量（FMBM、DMBM、g) 以新鲜物质 (FM) 或干物质 (DM) 为基础，以及在测试期间测量的咬合次数 (LB) 和咬合率 (LBR) 的对数。偏最小二乘回归分析 (PLSR) 用于验证加速度变量是否可以用作行为特征的预测因子。PLSR 的精确度和准确度是通过模型评估系统进行评估的，其中根据 r2 和 Dent & Blackie 检验将预测值与观察到的值进行回归。PLSR 显示出整体良好的准确度（Dent & Blackie 测试 P = ns），并且被证明对于 LB (r2 = 0.87)、LBR (r2 = 0.86)、DMI 和 DMIR (r2 = 0.71) 的估计是精确的。总而言之，BEHARUM 可以以高到中等的精度准确估计绵羊短期放牧地中海牧草的咬合次数和牧草摄入量。