Prediction of enteric methane emissions by sheep using an intercontinental database
Authors
Organisations
Type | Abstract |
---|
Original language | English |
---|---|
Pages | 61 |
Publication status | Published - 05 Jun 2022 |
Event | 8th International Greenhouse Gas & Animal Agriculture Conference - University of Florida, Orlando, Florida, United States of America Duration: 05 Jun 2022 → 09 Jun 2022 https://conference.ifas.ufl.edu/ggaa/index.html |
Conference
Conference | 8th International Greenhouse Gas & Animal Agriculture Conference |
---|---|
Abbreviated title | GGAA 2022 |
Country/Territory | United States of America |
City | Orlando, Florida |
Period | 05 Jun 2022 → 09 Jun 2022 |
Internet address |
Permanent link | Permanent link |
---|
Abstract
Enteric methane (CH4) emissions from sheep contribute to global greenhouse gas emissions from livestock. However, as already available for dairy and beef cattle, empirical models are needed to predict CH4 emissions from sheep for accounting purposes. The objectives of this study were to: 1) collate an intercontinental database of enteric CH4 emissions from individual sheep; 2) identify the key variables for predicting enteric sheep CH4 absolute production (g/d per animal) and yield [g/kg dry matter intake (DMI)] and their respective relationships; and 3) develop and cross-validate global equations as well as the potential need for age-, diet-, or climatic region-specific equations. The refined intercontinental database included 2,135 individual animal data from 13 countries. Linear CH4 prediction models were developed by incrementally adding variables. A universal CH4 production equation using only DMI led to a root mean square prediction error (RMSPE, % of observed mean) of 25.4% and an RMSPE-standard deviation ratio (RSR) of 0.69. Universal equations that, in addition to DMI, also included body weight (DMI + BW), and organic matter digestibility (DMI + OMD + BW) improved the prediction performance further (RSR, 0.62 and 0.60), whereas diet composition variables had negligible effects. These universal equations had lower prediction error than the extant IPCC 2019 equations. Developing age-specific models for adult sheep (>1-year-old) including DMI alone (RSR = 0.66) or in combination with rumen propionate molar proportion (for research of more refined purposes) substantially improved prediction performance (RSR = 0.57) on a smaller dataset. On the contrary, for young sheep (
Keywords
- Age, Diet composition, Climatic regions, Prediction models, Rumen fermentation