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Modelling methane emissions from remotely collected liveweight data and faecal near-infrared spectroscopy in beef cattle

机译:根据肉牛的远程采集的体重数据和粪便近红外光谱对甲烷排放进行建模

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The objective of the present study was to develop a model-data fusion approach using remotely collected liveweight (LW) data from individual animals (weighing station placed at the water trough) and evaluate the potential for these data from frequent weighing to increase the accuracy of estimates of methane emissions from beef cattle grazing tropical pastures. Remotely collected LW data were used to calculate daily LW change (LWC), i.e. growth rate on a daily basis, and then to predict feed intake throughout a 342-day grazing period. Feed intake and diet dry matter digestibility (DMD) from faecal near-infrared spectroscopy analysis were used to predict methane emissions using methods for both tropical and temperate cattle as used in the Australian national inventory (Commonwealth of Australia 2014). The remote weighing system captured both short-and long-term environmental (e.g. dry and wet season, and rainfall events) and management effects on LW changes, which were then reflected in estimated feed intake and methane emissions. Large variations in all variables, measured and predicted, were found both across animals and throughout the year. Methane predictions using the official national inventory model for tropical cattle resulted in 20% higher emissions than those for temperate cattle. Predicted methane emissions based on a simulation using only initial and final LW and assuming a linear change in LW between these two points were 7.5% and 5.8% lower than those using daily information on LW from the remote weighing stations for tropical and temperate cattle, respectively. Methane emissions and feed intake can be predicted from remotely collected LW data in near real-time on a daily basis to account for short-and long-term variations in forage quality and intake. This approach has the potential to provide accurate estimates of methane emissions at the individual animal level, making the approach suitable for grazing livestock enterprises wishing to participate in carbon markets and accounting schemes
机译:本研究的目的是开发一种模型数据融合方法,使用从单个动物(称重站位于水槽处)的远程收集的活重(LW)数据,并评估频繁称重这些数据的潜力,以提高准确性。估计热带牧场放牧的肉牛的甲烷排放量。远程收集的LW数据用于计算每日LW变化(LWC),即每天的增长率,然后预测整个342天放牧期间的采食量。粪便近红外光谱分析法的饲料摄入量和日粮干物质消化率(DMD)用于预测甲烷排放量,方法是使用澳大利亚国家清单中使用的热带和温带牛的方法(澳大利亚联邦,2014年)。远程称重系统捕获了短期和长期环境(例如,干燥和潮湿的季节以及降雨事件)以及对LW变化的管理影响,然后将其反映在估计的采食量和甲烷排放中。整个动物以及全年都发现,所有变量的测量值和预测值均存在较大差异。使用官方的国家库存量模型对热带牛的甲烷预测得出的排放量比温带牛的排放量高20%。根据仅使用初始和最终LW进行模拟并假设这两个点之间的LW线性变化的模拟预测的甲烷排放量分别比使用来自远程称重站的热带和温带牛的LW的每日信息分别低7.5%和5.8% 。甲烷的排放量和采食量可以每天从近乎实时的远程收集的LW数据中进行预测,以解释饲料质量和采食量的短期和长期变化。这种方法有可能提供有关单个动物水平的甲烷排放的准确估计值,从而使该方法适用于希望参与碳市场和会计制度的放牧牲畜企业

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