反刍动物生产系统的温室气体(GHG)排放的贡献在国家之间以及各个国家的地区之间有所不同。温室气体排放量的适当量化,特别是甲烷(CH4),提出了关于温室气体清单正确报告的问题,也许更重要的是,如何最好地减少CH4排放。本综述记录了现有的方法和方法,以测量和估计反刍动物的CH4排放量以及在各种规模和条件下产生的粪便。CH4的测量经常在研究环境中使用为生物能量目的开发的经典方法进行。如气体交换技术(呼吸室,流浆箱)。虽然非常精确,这些技术仅限于研究环境,因为它们很昂贵,劳动密集型,只适用于少数动物。Head-stalls,比如GreenFeed系统,已用于测量单独饲养或以小组饲养或放牧的单个动物的过期CH4。该技术需要在昼夜测量期间频繁的动物探访和足够数量的收集日。示踪气体技术可用于测量室外饲养的单个动物的CH4,因为需要确保低背景浓度。微气象技术(例如,开路激光器)可以测量更大区域和许多动物的CH4排放,但是存在限制,包括需要在更长时间内进行测量。粪便中CH4排放量的测量取决于储存类型,动物住房,感兴趣区域边界内外的CH4浓度,和通风率,这可能是对测量不确定度贡献最大的变量。对于大规模地区,飞机,无人机,卫星已经与示踪通量方法结合使用,逆建模,图像,和激光雷达(光检测和测距),但是在验证这些方法方面研究滞后。估算CH4排放量的自下而上方法依赖于经验或机械模型来量化单个来源(肠和粪便)的贡献。相比之下,自上而下的方法使用空间和时间模型来估算大气中CH4的含量,以考虑从发射器到观测点的运输。虽然这两种估计方法很少一致,它们有助于识别实践中的知识差距和研究要求。
需要准确和精确地量化温室气体(GHG)排放,特别是甲烷(CH4),确保温室气体清单的正确报告,也许更重要的是,确定如何最好地减少CH4排放。这项研究的目的是回顾现有的方法和方法来量化和估计反刍动物的CH4排放。历史上,大多数技术是为特定目的而开发的,这可能会限制其在商业农场和库存目的的广泛使用,并且通常需要频繁的校准和设备维护。整个动物和头部呼吸室,现场采样技术,和示踪气体方法可用于测量单个动物的肠道CH4,但是每种技术都有其固有的局限性。粪便中CH4排放量的测量取决于储存类型,动物住房,感兴趣区域边界内外的CH4浓度,和通风率,这可能是造成许多不确定性的最复杂的变量。对于大规模地区,飞机,无人机,卫星已经与示踪通量方法结合使用,逆建模,图像,和激光雷达(光检测和测距),但是在验证这些方法方面研究滞后。自下而上的方法来估计CH4排放量依赖于经验或机械模型来量化单个来源的贡献。自上而下的方法使用空间和时间模型来估计大气中CH4的量,以考虑从发射器到观测点的运输。
The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH4), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH4 emissions. This review documents existing methods and methodologies to measure and estimate CH4 emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH4 have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH4 for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH4 from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH4 emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH4 emissions from manure depends on the type of storage, animal housing, CH4 concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH4 emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources (enteric and manure). In contrast, top-down approaches estimate the amount of CH4 in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point. While these two estimation approaches rarely agree, they help identify knowledge gaps and research requirements in practice.
There is a need to accurately and precisely quantify greenhouse gas (GHG) emissions, specifically methane (CH4), to ensure correct reporting of GHG inventories and, perhaps more importantly, determine how to best mitigate CH4 emissions. The objective of this study was to review existing methods and methodologies to quantify and estimate CH4 emissions from ruminants. Historically, most techniques were developed for specific purposes that may limit their widespread use on commercial farms and for inventory purposes and typically required frequent calibration and equipment maintenance. Whole animal and head respiration chambers, spot sampling techniques, and tracer gas methods can be used to measure enteric CH4 from individual animals, but each technique has its own inherent limitations. The measurement of CH4 emissions from manure depends on the type of storage, animal housing, CH4 concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the most complex variable creating many uncertainties. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH4 emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources. Top-down approaches estimate the amount of CH4 in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point.