Department of Animal Science, The Pennsylvania State University, University Park 16802.
Department of Animal Science, University of California, Davis 91616.
J Dairy Sci. 2018 Jul;101(7):6655-6674. doi: 10.3168/jds.2017-13536. Epub 2018 Apr 19.
Ruminant production systems are important contributors to anthropogenic methane (CH) emissions, but there are large uncertainties in national and global livestock CH inventories. Sources of uncertainty in enteric CH emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH emission factors. There is also significant uncertainty associated with enteric CH measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes.
反刍动物生产系统是人为甲烷（CH）排放的重要贡献者，但在国家和全球牲畜 CH 清单中存在很大的不确定性。肠道 CH 排放的不确定性来源包括动物存栏量、饲料干物质摄入量（DMI）、饲料的成分和化学组成以及 CH 排放因子。肠道 CH 测量也存在很大的不确定性。最广泛使用的技术是呼吸室、六氟化硫（SF）示踪剂技术和自动化头部室系统（GreenFeed；C-Lock Inc.，拉皮德城，SD）。所有 3 种方法都已成功用于在各种环境条件下对奶牛或肉牛进行的大量实验中，尽管比较这些技术的研究报告结果不一致。尽管已经开发了不同类型的模型来预测肠道 CH 排放，但与更详细的经验和基于过程的机械模型相比，由于其广泛的适用性和易用性，相对简单的经验（统计）模型通常用于清单目的。然而，现有的用于预测肠道 CH 排放的经验模型存在空间焦点狭窄、观测有限以及所使用的统计技术的局限性。因此，预测模型必须基于可以通过世界各地科学家合作生成的强大数据集来开发。为了实现高预测精度，这些数据集应涵盖区域内和全球范围内的各种饮食和生产系统。总的来说，肠道 CH 预测模型基于各种动物或饲料特征输入，但以某种形式的 DMI 为主。因此，准确预测 DMI 对于准确预测牲畜 CH 排放至关重要。对大量奶牛个体数据的分析表明，基于 DMI 或 DMI 加上有限的饲料或动物相关输入的简化肠道 CH 预测模型可以以与更复杂的经验模型相似的准确性预测平均 CH 排放。这些简化模型可可靠地用于排放清单目的。
