Classification of sheep urination events using accelerometers to aid improved measurements of livestock contributions to nitrous oxide emissions
Classification of sheep urination events using accelerometers to aid improved measurements of livestock contributions to nitrous oxide emissions
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DOI：
10.1016/j.compag.2018.04.018
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发表时间：
2018-07-01
影响因子：
8.3
通讯作者：
King, Andrew J.
中科院分区：
文献类型：
--
作者：
Lush, Lucy;Wilson, Rory P.;King, Andrew J.
在英国，畜牧业排放的一氧化二氮占温室气体的74%。然而，目前尚不确定有多少直接归因于局部绵羊排尿事件，这可能会产生一氧化二氮排放“热点”。目前，气专委的排放系数主要是从低地放牧系统推断出来的，没有纳入与绵羊行为和移动有关的时间或空间因素。能够收集可靠测量绵羊何时、何地以及排尿量的数据对于精确计算是必要的，并为减少温室气体排放和最大限度地减少基于排放的气候变化提供最佳管理实践。动物附着的运动传感器已被证明可以有效地对不同行为进行分类，尽管分类精度取决于行为类型。以前的研究已经使用加速度计的牛和羊，以评估积极和非积极的行为，以帮助放牧管理，虽然还没有研究试图确定羊排尿事件使用这种方法。我们附加三轴加速度传感器标签30威尔士山母羊30天，以评估我们是否可以识别排尿事件。我们使用随机森林模型，使用不同的滑动均值窗口来分类行为。排尿有一个独特的模式，可以从加速度计数据中识别，5秒的窗口提供最好的回忆，10秒的窗口提供最好的精度。考虑的“状态”行为（觅食，行走，跑步，站立和躺下）也被识别为具有高回忆率和精确度。这表明，离散“事件”行为的识别可能对用于计算汇总统计量的窗口大小敏感。该方法有望识别绵羊和其他牲畜的排尿，与其他方法相比具有微创性，并具有明确的潜力为农业管理实践和政策提供信息。
Livestock emissions account for 74% of nitrous oxide contributions to greenhouse gases in the UK. However, it remains uncertain how much is directly attributable to localised sheep urination events, which could generate nitrous oxide emission `hot spots'. Currently, IPCC emission factors are mainly extrapolated from lowland grazing systems and do not incorporate temporal or spatial factors related to sheep behaviour and movement. Being able to gather data that reliably measures when, where, and how much sheep urinate is necessary for accurate calculations and, to inform best management practices for reducing greenhouse gas emissions and minimizing emission-based climate change.Animal-attached movement sensors have been shown to be effective in classifying different behaviours, albeit with varying classification accuracy depending on behaviour types. Previous studies have used accelerometers on cattle and sheep to assess active and non-active behaviours to help with grazing management, although no study has yet attempted to identify sheep urination events using this method.We attached tri-axial accelerometer sensor tags to thirty Welsh Mountain ewes for thirty days to assess if we could identify urination events. We used random forest models using different sliding mean windows to classify behaviours. Urination had a distinctive pattern and could be identified from accelerometer data, with a 5 s window providing the best recall and a 10 s window giving the best precision. 'State' behaviours considered (foraging, walking, running, standing and lying down) were also identified with high recall and precision. This demonstrates the extent to which the identification of discrete `event' behaviours may be sensitive to the window size used to calculate the summary statistics. The method shows promise for identifying urination in sheep and other livestock, being minimally invasive compared to other methods, and has clear potential to inform agricultural management practices and policies.