GLOBAL NETWORK FOR THE DEVELOPMENT AND MAINTENANCE OF NUTRITION-RELATED STRATEGIES FOR MITIGATION OF METHANE AND NITROUS OXIDE EMISSIONS

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: COMPLETE
• Funding Source: AFRI COMPETITIVE GRANT
• Reporting Frequency: Annual
• Accession No.: 1003050
• Grant No.: 2014-67003-21979
• Cumulative Award Amt.: $100,000.00
• Proposal No.: 2014-01837
• Project Start Date: Jul 1, 2014
• Project End Date: Jun 30, 2019
• Grant Year: 2014
• Program Code: [A3161]- Interagency Climate Change NASA

Recipient Organization
PENNSYLVANIA STATE UNIVERSITY
408 Old Main
UNIVERSITY PARK,PA 16802-1505

Performing Department
Animal Science

Non Technical Summary
Ruminant husbandry is a major source of anthropogenic greenhouse gases (GHG). There is a large body of existing nutrition-related GHG and ammonia (NH3) mitigation data that are not well organized.The main objective of the GLOBAL NETWORK consortium, a 4-yr project funded through The Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI), is to accumulate and analyze ruminant GHG mitigation data.The specific goals of this collaborative project are to: (1) Create, update, and expand animal and feed databases for the mitigation of enteric methane (CH4); (2) Gain understanding of the contribution of genetic and microbial factors to the variation in enteric CH4 production, digestion, and nutrient utilization; (3) Validate markers of enteric methanogenesis for the development and monitoring of CH4 mitigation strategies in ruminants; (4) Create, update, and expand a database of mitigation strategies aimed at improving dietary N utilization and lowering N excretion and decreasing NH3 and nitrous oxide (N2O) emissions from manure; (5) Develop Standard Operating Procedures (SOP) and guidelines for conducting and assessing data from in vitro and in vivo studies designed to evaluate nutritional strategies for mitigation of CH4, NH3, and N2O emissions; (6) Develop new and evaluate existing models for predicting CH4 emission and N excretions under various nutritional, animal, and farm management scenarios; and (7) Identify and recommend CH4, NH3, and N2O mitigation technologies that are both practical and feasible for implementation in various ruminant livestock production systems.These activities will be integrated with those of the "Network and Database on Feed and Nutrition in Relation to Greenhouse Gas Emissions" (FNN; http://animalscience.psu.edu/fnn), which is an activity of the Livestock Research Group (LRG) of the Global Research Alliance (GRA) on Agricultural Greenhouse Gases. The newly created GLOBAL NETWORK consortium intends to fill important knowledge gaps and provide the much needed expert recommendations for future research priorities, methodologies, and science-based GHG mitigation solutions to governments and non-governmental organizations, advisory/extension networks, and the ruminant livestock sector.

Animal Health Component

80%

Research Effort Categories

Basic

20%

Applied

80%

Developmental

(N/A)

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
305	3410	1010	100%

Knowledge Area
305 - Animal Physiological Processes;

Subject Of Investigation
3410 - Dairy cattle, live animal;

Field Of Science
1010 - Nutrition and metabolism;

Keywords

ammonia

database

greenhouse gas

livestock

mitigation

Goals / Objectives
The main objective of the GLOBAL NETWORK consortium, a 4-yr project funded through The Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI), is to accumulate and analyze ruminant GHG mitigation data. The specific goals of this collaborative project are to: (1) Create, update, and expand animal and feed databases for the mitigation of enteric methane (CH4); (2) Gain understanding of the contribution of genetic and microbial factors to the variation in enteric CH4 production, digestion, and nutrient utilization; (3) Validate markers of enteric methanogenesis for the development and monitoring of CH4 mitigation strategies in ruminants; (4) Create, update, and expand a database of mitigation strategies aimed at improving dietary N utilization and lowering N excretion and decreasing NH3 and nitrous oxide (N2O) emissions from manure; (5) Develop Standard Operating Procedures (SOP) and guidelines for conducting and assessing data from in vitro and in vivo studies designed to evaluate nutritional strategies for mitigation of CH4, NH3, and N2O emissions; (6) Develop new and evaluate existing models for predicting CH4 emission and N excretions under various nutritional, animal, and farm management scenarios; and (7) Identify and recommend CH4, NH3, and N2O mitigation technologies that are both practical and feasible for implementation in various ruminant livestock production systems.

Project Methods
The activities of this project will be integrated with those of the "Network and Database on Feed and Nutrition in Relation to Greenhouse Gas Emissions" (FNN; http://animalscience.psu.edu/fnn), which is an activity of the Livestock Research Group (LRG) of the Global Research Alliance (GRA) on Agricultural Greenhouse Gases.

Progress 07/01/14 to 06/30/19

Outputs
Target Audience:Academia, extension educators, dairy consultants, feed industry, dairy producers. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Collaboration with lead scientists around the world. How have the results been disseminated to communities of interest?Publications in lead scientici journals and presentations at scientific conferences. What do you plan to do during the next reporting period to accomplish the goals?This is a final report, The project expired.

Impacts
What was accomplished under these goals? The GLOBAL NETWORK (GN) project was established to collate and analyze methane emission and mitigation data for ruminants. Two separate databases have been developed: mitigation database and prediction database. The objective of the mitigation database is to summarize and recommend science-based enteric methane mitigation options to stakeholders. This database consists of 1,800 experimental treatment means from 410 publications. The goal of the prediction database, which consists of individual animal data, is to develop robust enteric methane emission prediction models for various ruminant species (dairy and beef cattle, sheep) and nutritional, animal, and farm management scenarios. Three sub-databases within the prediction database were developed: (dairy cattle, (2) beef cattle, and (3) small ruminants; in addition, (4) an accross species, microbial databse was also developed. The dairy cattle prediction database currently contains 5,899 individual animal observations from 159 studies from North and South America, Europe, and Oceania. Development of enteric methane prediction models was conducted using a sequential approach, by incrementally adding available information to develop models with increasing complexity. In total, 11 models were developed. Methane emission (g/d, per DMI, or per milk/energy-corrected milk yields) was predicted by fitting linear mixed models including random effect of study nested within the random effect of continent. As expected, a global methane emission (g/d) model with a greater number of independent variables fitted the data best [Root mean square prediction error as a percentage of mean observed value (RMSPE) = 13.4%]. Inputs were DMI, dietary concentrations of ether extract (EE) and NDF, milk fat and protein content, and cow BW. The predictive ability of fitted models was evaluated through cross-validation. Less complex models requiring only DMI, or DMI plus NDF or EE concentrations had predictive ability similar to more complex models (RMSPE = 14.0 to 14.3%). These prediction models, along with recommendations from the mitigation database analysis, provide robust enteric methane inventory and mitigation options for ruminant farming systems.

Publications

• Type: Journal Articles Status: Published Year Published: 2018 Citation: Niu, M., E. Kebreab, A. N. Hristov, J. Oh, C. Arndt, A. Bannink, A. R. Bayat, A. F. Brito, T. Boland, D. Casper, L. A. Crompton, J. Dijkstra, M. A. Eug�ne, P. C. Garnsworthy, M. N. Haque, A. L. F. Hellwing, P. Huhtanen, M. Kreuzer, B. Kuhla, P. Lund, J. Madsen, C. Martin, S. C. McClelland, M. McGee, P. J. Moate, S. Muetzel, C. Mu�oz, P. OKiely, N. Peiren, C. K. Reynolds, A. Schwarm, K. J. Shingfield, T. M. Storlien, M. R. Weisbjerg, D. R. Y��ez-Ruiz, and Z. Yu. 2018. Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database. Global Change Biology doi: 10.1111/gcb.14094
• Type: Journal Articles Status: Published Year Published: 2018 Citation: Hristov, A. N., E. Kebreab, M. Niu, J. Oh, C. Arndt, A. Bannink, A. R. Bayat, A. F. Brito, D. Casper, L. A. Crompton, J. Dijkstra, P. C. Garnsworthy, N. Haque, A. L. F. Hellwing, P. Huhtanen, M. Kreuzer, B. Kuhla, P. Lund, J. Madsen, S. C. McClelland, P. Moate, C. Mu�oz, N. Peiren, J. M. Powell, C. K. Reynolds, A. Schwarm, K. J. Shingfield, T. M. Storlien, M. R. Weisbjerg, Z. Yu, T.B. Boland, C. Martin, M. Eug�ne, D. R. Y��ez-Ruiz, S. Muetzel. 2018. Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models. J. Dairy Sci. doi.org/10.3168/jds.2017-1353
• Type: Journal Articles Status: Published Year Published: 2016 Citation: Y��ez-Ruiz D. R., A. Bannink, J. Dijkstra, E. Kebreab, D. Morgavi, P. O�Kiely, C. K. Reynolds, A. Schwarm, K. Shingfield, Z. T. Yu, and A. N. Hristov. 2016. Design, implementation and interpretation of in vitro batch culture experiments to assess methane mitigation in ruminants  a review (Anim. Feed Sci. Technol. 216:1-18)
• Type: Journal Articles Status: Published Year Published: 2016 Citation: Hammond, K. J., L. A. Crompton, A. Bannink, J. Dijkstra, D. R. Y��ez-Ruiz, P. OKiely, E. Kebreab, M. A. Eugen�, Z. Yu, K. J. Shingfield, A. Schwarm, A. N. Hristov, C. K. Reynolds. 2016. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminant (Anim. Feed Sci. Technol. 219:13-30)
• Type: Journal Articles Status: Under Review Year Published: 2019 Citation: 2. van Lingen, H. J., Mutian Niu, Ermias Kebreab, Sebasti�o C. Valadares Filho, John A. Rooke, Angela Schwarm, Michael Kreuzer, Phil I. Hynd, Mariana Caetano, Maguy Eug�ne, Cecile Martin, Mark McGee, Padraig O'Kiely, Martin H�nerberg, Tim A. McAllister, Telma T. Berchielli, Juliana D. Messana, Nico Peiren, Alex V. Chaves, Ed Charmley, N. Andy Cole, Kristin E. Hales, Sang-Suk Lee, Alexandre Berndt, Christopher K. Reynolds, Les A. Crompton, Ali-Reza Bayat, David R. Y��ez-Ruiz, Zhongtang Yu, Andr� Bannink, Jan Dijkstra, David P. Casper, A. N. Hristov. 2019 Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database. Agriculture, Ecosystems & Environment (under review).
• Type: Journal Articles Status: Under Review Year Published: 2019 Citation: 3. Benaouda, M., Xinran Li, C�cile Martin, Ermias Kebreab, A. N. Hristov, Zhongtang Yu, David R. Y��ez-Ruiz, Christopher K. Reynolds, Les A. Crompton, Jan Dijkstra, Andr� Bannink, Angela Schwarm, Michael Kreuzer, Mark McGee, Peter Lund, Anne L. F. Hellwing, Martin R. Weisbjerg, Peter J. Moate, Ali R. Bayat, Kevin J. Shingfield, Nico Peiren, and Maguy Eug�ne. 2019. Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: animal categories and dietary mitigation strategies. Anim. Feed Sci. Technol. (under review).

Progress 07/01/14 to 06/30/15

Outputs
Target Audience:Academia, extension educators, dairy consultants, feed industry, dairy producers. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?By publishing a major paper: Niu, M., E. Kebreab, A. N. Hristov, J. Oh, C. Arndt, A. Bannink, A. R. Bayat, A. F. Brito, T. Boland, D. Casper, L. A. Crompton, J. Dijkstra, M. A. Eugène, P. C. Garnsworthy, M. N. Haque, A. L. F. Hellwing, P. Huhtanen, M. Kreuzer, B. Kuhla, P. Lund, J. Madsen, C. Martin, S. C. McClelland, M. McGee, P. J. Moate, S. Muetzel, C. Muñoz, P. O'Kiely, N. Peiren, C. K. Reynolds, A. Schwarm, K. J. Shingfield, T. M. Storlien, M. R. Weisbjerg, D. R. Yáñez-Ruiz, and Z. Yu. 2017. Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database. Global Change Biology doi: 10.1111/gcb.14094. In addition, a symposium "Greenhouse Gas Emissions from Dairy Operations" was held in conjunction with the American Dairy Science Association convention. What do you plan to do during the next reporting period to accomplish the goals?We continue working on the beef and small ruminants databases. We are aslo working on a review paper "NITROGEN IN RUMINANT NUTRITION: A REVIEW OF MEASUREMENT TECHNIQUES". Another manuscript originating from the GLOBAL NETWORK project has been submitted to J Dairy Sci (UNCERTAINTIES IN ENTERIC METHANE INVENTORIES, MEASUREMENT TECHNIQUES, AND PREDICTION MODELS).

Impacts
What was accomplished under these goals? The dairy cattle enteric methane database work was completed and a major publication (see above) was published in Global Change Biology. Work on methane databases for beef cattle and small ruminants (sheep and goats) is continuing.

Publications

• Type: Journal Articles Status: Published Year Published: 2017 Citation: 5. Niu, M., E. Kebreab, A. N. Hristov, J. Oh, C. Arndt, A. Bannink, A. R. Bayat, A. F. Brito, T. Boland, D. Casper, L. A. Crompton, J. Dijkstra, M. A. Eug�ne, P. C. Garnsworthy, M. N. Haque, A. L. F. Hellwing, P. Huhtanen, M. Kreuzer, B. Kuhla, P. Lund, J. Madsen, C. Martin, S. C. McClelland, M. McGee, P. J. Moate, S. Muetzel, C. Mu�oz, P. OKiely, N. Peiren, C. K. Reynolds, A. Schwarm, K. J. Shingfield, T. M. Storlien, M. R. Weisbjerg, D. R. Y��ez-Ruiz, and Z. Yu. 2017. Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database. Global Change Biology doi: 10.1111/gcb.14094.