The enteric methane emissions from dairy and beef cattle are considered as a major contributor of greenhouse gases emissions in U.S. Since enteric methane emission represents an unproductive loss of dietary energy, one of the predominant methane emission estimation procedures are driven by first estimating daily gross energy intake (GEI) by individual animals and then multiplying it by an estimate of “methane conversion factor (Ym)”, which is in the range of 4–10% of GEI. The IPCC Tier 2 enteric methane emission estimation procedures are driven by first estimating daily and annual gross energy consumption by individual animals within an inventory class which are then multiplied by an estimate of CH4 loss per unit of feed (Ym). The extent to which feed energy is converted to CH4 depends on several interacting feed and animal factors. It is important to examine the influences of feed properties and animal attributes on Ym. Such influences are important to better understand the microbiological mechanisms involved in methanogenesis with a view to designing emission abatement strategies, as well as to identify different values for Ym according to animal husbandry practices. The search for influences of feed properties and animal attributes on Ym is not sufficiently documented and sometimes equivocal. There is considerable room for improvement in the IPCC Tier 2 prediction in Ym. As more data are collected, a meta-analysis may better determine the influential variables. The objective of this study was to conduct a systematic literature review and meta-analysis in order to identify and quantify the sources of the variability and uncertainty in reported Ym values, and in particular the influence of feed and animal properties upon Y
What did we do?
Multiple strategies were undertaken to identify potentially eligible studies to be included in the meta-analysis. The inclusion criteria were: the study must have reported measured CH4 emission data which can be expressed in the form of Ym; and the study must be published in a peer reviewed journal in English. The selected studies were distributed to a group of trained analysts for data extraction. Standard data extraction sheets were developed for consistency. As a result of the data review and extraction processes, a meta-analysis dataset was created. The dataset for the meta-analysis included all control treatment means at various common feed and animal combinations. Some studies provided treatment means at different conditions; in these cases, more than one treatment means (data points) were extracted from one study. Treatment means for special feed additive treatments were not included. Data across studies were analyzed statistically using the MIXED procedures of SAS (SAS for Windows, Version 9.3, SAS Institute, Cary, NC). Model development was conducted in a meta-analytical manner by treating study effect as random. The numbers of animals contributing to each treatment mean were used as a weighting variable. Various processes were used to test for confounding terms. Significant effects were declared at P < 0.05.
What have we learned?
The literature search efforts yielded a total of 75 peer reviewed studies that provided measured enteric CH4 emissions from beef or dairy cattle operations, which were expressed as Ym. These studies included 184 treatment means at various animal and feed combinations.The CH4 emission rates expressed in g/animal/day were positively related with weight of animal (P<0.01), and they showed a bimodal distribution, which could be due to the weight difference between dairy and beef cattle. The CH4 emission rates expressed in Ym, or g/kg DMI were more close to have a normal distribution, and they have much less variation compared with CH4 emission rates expressed in g/animal/day. The Ym values were significantly affected by feeding style (grazing vs. housed, P<0.01) and cattle type (dairy vs. beef, P<0.01), and an interaction of feeding style and cattle type was observed (P<0.01). The Ym for beef had large variation than the Ym for dairy cattle. Grazing beef had the largest mean value of Ym. For housed cattle, no significant difference was observed between beef and dairy (P=0.54).Forage content in diet significantly affect the Ym values (P<0.01), while effect of geography region was not significant at 0.05 level (P=0.06). For grazing cattle, significant higher Ym was observed for beef cattle as compared to dairy cattle (7.9% vs. 6.1%, P=0.02). The effect of diet forage content on Ym could be explained by the feed digestibility. It was found Ym was negatively related with the general energy intake (GEI) of cattle per kg of body weight (P<0.01), or the OM digestibility of feed (P=0.01). The higher the OM digestibility of feed, the higher GEI per kg of body weight, and the lower the Ym value. The OM digestibility of feed and the GEI per kg of body weight were positively related with each other and may not be independent. When both of them were included in the model of Ym, only the OM digestibility of feed was significant. A model was obtained for estimating Ym from the OM digestibility of feed. The reported fat content in diet ranged from 18 to 64 g per kg of dry matter. The Ym value was negatively related with the fat content in diet. Although the effect was not statistically significant in this meta-analysis (P=0.31), it confirmed the hypothesis that increasing fat content in diet can potentially result in reduced CH4 emission. The effect of lactation status on Ym was examined for dairy cattle, including both grazing and housed animals. Lactating dairy cattle tend to have lower Ym than dry one (6.5% vs. 7.0%). However, the effect was not statistically significant (P=0.32). The days in milk for lactating dairy cattle showed no significant effect on Ym values (P=0.39).
- Identify research gaps in estimation of Ym values in literature. Quantify the uncertainties and highlight the main source of variation.
- Refine the Ym estimation model.
- Based on the results, develop suggestions or guidelines to improve feed efficiency and to reduce carbon footprint per unit of product
Zifei Liu, Assistant professor, Kansas State University firstname.lastname@example.org
Yang Liu, Xiuhuan Shi
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