Feeding intake estimation in sheep based on ingestive chewing sounds
Feeding intake estimation in sheep based on ingestive chewing sounds
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基于咀嚼声音的绵羊采食量估算
DOI：
10.1016/j.compag.2022.106698
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发表时间：
2022-03
影响因子：
8.3
通讯作者：
Xiantao Fan
中科院分区：
文献类型：
--
作者：
Kui Wang;Chuanzhong Xuan;Pei Wu;Feilong Liu;Xiantao Fan
准确估计绵羊采食量对于优化牧场管理和了解草畜生态系统模式至关重要。虽然有许多研究报道，但很少有研究综合考虑动物体重，草种和草含水量。本研究设计了三个绵羊体重等级、两个草种、三个牧草含水量水平组合的29个试验。从每个测试中构建片段样本，并从每个片段样本中提取与咀嚼相关的许多解释变量（共44个）。在每个测试下检查每个解释变量和摄入量之间的相关性，并记录其回归线的斜率。然后，用统计分析来揭示羊体重类，草种，草含水量类的解释变量的斜率的影响。在测试样本集上（一个测试样本对应一个测试），使用单个解释变量、所有解释变量、所有解释变量和因子变量（绵羊体重、草水分含量、草NDF含量、草ADF含量）预测摄入量。结果表明，chew_ZCT OverLegth变量（咀嚼波形的数量除以波形长度，然后在所有咀嚼中累积）和摄入量具有最强的相关性，平均R2得分为0.8796。牧草含水率等级对变量斜率的影响与绵羊体重等级的影响大致相当，小于个体差异的影响。在测试样本集中，LogMel_power变量（波形log-Mel的平方和）是预测摄入量的最佳单个解释变量（当摄入量类型为新鲜草物质时）。添加因子变量显著增加了模型的R2得分，减少了使用的变量数量。将绵羊体重引入摄入量进一步增加了模型的R2评分，并减少了使用的变量数量。当采食量类型为FMI_W（新鲜物质乘以绵羊体重值的平方）时，利用所有解释变量和因子变量均能准确估计采食量，R2值为0.9734。这一结果表明，尽管实验条件的复杂性，咀嚼衍生的声学变量声学上可以准确地估计摄入量。这项研究将作出突出贡献的发展一般和准确的模型估计摄入量。
Accurate estimation of sheep feed intake is essential for optimal pasture management and understanding the patterns of grass-livestock ecosystems. Although many studies have been reported, fewer studies have comprehensively considered animal body weight, grass species, and grass moisture content. This study designed a 29-test experiment with three classes of sheep weight, two grass species, and three levels of grass moisture content combinations. Segment samples were constructed from each test, and many explanatory variables (44 in total) associated with chewing were extracted from each segment sample. Correlations between each explanatory variable and intake were examined under every test, and the slopes of their regression lines were also recorded. A statistical analysis was then used to reveal the effects of sheep bodyweight class, grass species, and grass moisture content class on the slopes of the explanatory variables. On the test sample set (one test sample corresponding to one test), intake was predicted using a single explanatory variable, all explanatory variables, all explanatory variables and factor variables (sheep weight, grass moisture content, grass NDF content, grass ADF content). The results showed that thechew_ZCTOverLegthvariable (number of chewing waveforms over the mean divided by waveform length and then accumulated over all chews) and intake had the strongest correlation with a mean R2score of 0.8796. The influence of grass moisture content class on the slope of the variable was approximately equal to that of the sheep weight class and less than that of the differences in individual sheep. In the test sample set, theLogMel_powervariable (sum of squares of waveform log-Mel) was the best single explanatory variable for predicting intake (when the type of intake is fresh grass matter). Adding factor variables significantly increased the R2score of the model and reduced the number of variables used. Introducing sheep bodyweight to intake further increased the R2score of the model and reduced the number of variables used. When the type of intake wasFMI_W(fresh matter multiplied by the square of the sheep bodyweight value), intake was accurately estimated using all explanatory and factor variables with an R2score of 0.9734. This result demonstrates that chewing-derived acoustic variables acoustically can accurately estimate intake despite the complexity of the experimental conditions. This study will make an outstanding contribution to the development of general and accurate models for estimating intake.
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影响因子：
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