Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Microbes and Methanogenesis01:26

Microbes and Methanogenesis

Methanogenesis is a critical microbial process in anaerobic ecosystems responsible for the biological production of methane, a potent greenhouse gas and valuable biofuel. This metabolic pathway is primarily facilitated by methanogenic archaea, which thrive in anoxic environments such as wetlands, sediments, and animal gastrointestinal tracts. The absence of oxygen in these habitats prevents aerobic respiration, thereby favoring alternative biochemical pathways for organic matter degradation.In...
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Overview of Archaea01:29

Overview of Archaea

Archaea, named after the Archaean eon, represent a unique domain of life, distinct from bacteria and eukaryotes, with remarkable traits. Their cellular and molecular features, ecological adaptability, and industrial relevance highlight their importance in understanding life processes and leveraging biotechnology.Cellular and Molecular CharacteristicsA defining feature of archaea is their unique membrane composition. Archaeal membranes contain ether-linked isoprenoid lipids, which confer...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Invited review: Perspectives on nitrogen in dairy cattle nutrition.

Journal of dairy science·2026
Same author

Lactational performance and enteric methane emissions in dairy cows fed high-oil oats, cold-pressed rapeseed cake, and 3-nitrooxypropanol in a grass silage-based diet.

Journal of dairy science·2025
Same author

Asparagopsis taxiformis supplementation to mitigate enteric methane emissions in dairy cows-Effects on performance and metabolism.

Journal of dairy science·2025
Same author

Predicting CO<sub>2</sub> production of lactating dairy cows from animal, dietary, and production traits using an international dataset.

Journal of dairy science·2024
Same author

A meta-analysis of methane-mitigation potential of feed additives evaluated in vitro.

Journal of dairy science·2024
Same author

Editorial: The 10th international Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT).

Animal : an international journal of animal bioscience·2024

Related Experiment Video

Updated: May 14, 2026

The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals
11:02

The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Published on: September 7, 2015

Development of equations for predicting methane emissions from ruminants.

M Ramin1, P Huhtanen1

  • 1Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden.

Journal of Dairy Science
|February 14, 2013
PubMed
Summary

Ruminant methane (CH4) production is influenced by diet and intake. Feed intake is the primary driver of total CH4 output, with diet digestibility and fat concentration playing key roles.

More Related Videos

Design and Use of a Full Flow Sampling System (FFS) for the Quantification of Methane Emissions
08:18

Design and Use of a Full Flow Sampling System (FFS) for the Quantification of Methane Emissions

Published on: June 12, 2016

Assessment of Methane and Nitrous Oxide Fluxes from Paddy Field by Means of Static Closed Chambers Maintaining Plants Within Headspace
09:03

Assessment of Methane and Nitrous Oxide Fluxes from Paddy Field by Means of Static Closed Chambers Maintaining Plants Within Headspace

Published on: September 6, 2018

Related Experiment Videos

Last Updated: May 14, 2026

The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals
11:02

The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Published on: September 7, 2015

Design and Use of a Full Flow Sampling System (FFS) for the Quantification of Methane Emissions
08:18

Design and Use of a Full Flow Sampling System (FFS) for the Quantification of Methane Emissions

Published on: June 12, 2016

Assessment of Methane and Nitrous Oxide Fluxes from Paddy Field by Means of Static Closed Chambers Maintaining Plants Within Headspace
09:03

Assessment of Methane and Nitrous Oxide Fluxes from Paddy Field by Means of Static Closed Chambers Maintaining Plants Within Headspace

Published on: September 6, 2018

Area of Science:

  • Animal Science
  • Ruminant Nutrition
  • Environmental Science

Background:

  • Ruminants are significant sources of methane (CH4), a potent greenhouse gas, through enteric fermentation.
  • Accurate prediction of CH4 production is crucial for mitigating its environmental impact.

Purpose of the Study:

  • To develop and validate predictive equations for CH4 production in ruminants.
  • To evaluate the influence of dietary components and animal factors on CH4 emissions.

Main Methods:

  • A comprehensive dataset was compiled from 52 published respiration studies, including 207 cattle and 91 sheep diets.
  • Mixed-model regression analysis was employed to identify key predictors of CH4 energy output (CH4-E/GE) and total CH4 production (L/d).
  • Diets with >75% concentrate on a dry matter basis were excluded to focus on typical dairy cow rations.

Main Results:

  • The best-fit equation for CH4-E/GE included dry matter intake per body weight (DMIBW), organic matter digestibility (OMDm), ether extract (EE), neutral detergent fiber (NDF), and non-fiber carbohydrates (NFC).
  • Total CH4 production (L/d) was primarily determined by dry matter intake (DMI), with OMDm, EE intake, and NFC/(NDF + NFC) ratios also influencing the model.
  • Cross-validation confirmed the accuracy of the CH4-E/GE prediction equation (R²=0.85).

Conclusions:

  • Feed intake is the principal determinant of total methane production in ruminants.
  • Methane energy output is inversely related to feeding level and dietary fat concentration, and positively related to diet digestibility.
  • Dietary carbohydrate composition has a minimal impact on methane production relative to other factors.