Source: PENNSYLVANIA STATE UNIVERSITY submitted to
NETWORK FOR MITIGATION OF ENTERIC METHANE, AMMONIA, AND NITROUS OXIDE EMISSIONS FROM RUMINANT LIVESTOCK
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1019000
Grant No.
2019-67019-29400
Cumulative Award Amt.
$450,000.00
Proposal No.
2018-06988
Multistate No.
(N/A)
Project Start Date
Apr 15, 2019
Project End Date
Apr 14, 2024
Grant Year
2019
Program Code
[A1490]- BNRE Networks for Synthesis, Data Sharing and Management
Project Director
Hristov, A. N.
Recipient Organization
PENNSYLVANIA STATE UNIVERSITY
408 Old Main
UNIVERSITY PARK,PA 16802-1505
Performing Department
Animal Science
Non Technical Summary
The livestock sector contributes 48% of the total agricultural greenhouse gas (GHG) emissions and is the largest single source of methane in the U.S. The main goal of this proposal is to continue and expand the project team's on-going international activities related to mitigation of gaseous emissions (methane, ammonia, and nitrous oxide) from ruminant systems. The project will deliver databases and analyses critically-important for predicting and mitigating livestock GHG and ammonia emissions. Specifically, project activities will result in the development of the following databases: ENTERIC METHANE PREDICTION, ENTERIC METHANE MITIGATION, NITROGEN EMISSION PREDICTION, and NITROGEN EMISSION MITIGATION. The databases will contain individual animal data and treatment means from published and unpublished data provided by network collaborators. Each database will contain sub-databases for the main ruminant species (dairy, beef, and small ruminants). These network activities will: (1) allow development of robust and reliable enteric methane, ammonia, and nitrous oxide emissions prediction models; (2) allow development of regional or country specific emission factors that can be used at IPCC Tier 2 or 3 levels for more accurate accounting of GHG and ammonia emissions; (3) provide to stakeholders scientifically-based recommendations on effective and feasible GHG and ammonia mitigation practices; and (4) allow quantifying GHG and ammonia emission reduction through dietary, feed additive, or other mitigation options. As part of the above activities, U.S. scientists will actively participate in the CEDERS project (a large multi-national network project funded through the European Joint Programming Initiative on Agriculture, Food Security and Climate Change, FACCE ERA-GAS).
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
30239991010100%
Knowledge Area
302 - Nutrient Utilization in Animals;

Subject Of Investigation
3999 - Animal research, general;

Field Of Science
1010 - Nutrition and metabolism;
Goals / Objectives
The long-term goal of the current proposal is to continue and expand our on-going international activities related to mitigation of gaseous emissions (methane, ammonia, and nitrous oxide) from ruminant systems.Specific objectives within this long-term goal include:Continue and expand activities related to building Enteric Methane Prediction (EMP) databases of individual animal data (analysis of the DAIRY cattle database has already been completed and published in Global Change Biology; https://doi.org/10.1111/gcb.14094); work on the BEEF and SMALL RUMINANTS individual animal databases continues) and cross-validate the prediction equations. Such databases allow regional or country specific emission factors to be developed and used at IPCC Tier 2 or 3 levels for more accurate accounting of GHG emissions. An important part of these activities is to complete the on-going work on a separate database (Enteric Methane Mitigation, EMM) that is designed to provide to stakeholders scientifically-based recommendations on effective and feasible methane mitigation practices. The EMM database will allow quantifying GHG emission reduction through dietary, feed additive, or other mitigation options, which is not captured at the moment;Develop a Nitrogen Emission Prediction (NEP) database of individual animal data that will integrate animal nutrition, manure composition, and nitrogen emissions (nitrate leaching, ammonia, and nitrous oxide emissions) data and will be used to predict nitrogen emissions from cattle and small ruminants manure and evaluate mitigation strategies. Develop a separate Nitrogen Emission Mitigation (NEM) database that is designed to provide to stakeholders scientifically-based recommendations on effective and feasible nitrogen (nitrate, ammonia, nitrous oxide) mitigation practices. This NEM database will allow quantifying changes in nitrogen flux through implementation of dietary, feed additive, or other mitigation options;Involve U.S. scientists in the CEDERS (Capturing Effects of Diet on Emissions from Ruminant System) project - a large multi-national network project funded through the ERA-NET co-fund for monitoring & mitigation of GHG from agri- and silvi-culture of the European Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE ERA-GAS).
Project Methods
Objective 1: the BEEF EMP database within Objective 1 developed in the frame of the GLOBAL NETWORK project already contains 2,015 individual beef cattle records from 52 published and unpublished studies conducted from 1969 to 2015 by researchers and research institutes from Europe (n = 869), North America (n = 649), Brazil (n = 313), Australia (n = 174) and South Korea (n = 10). Methane production, yield and intensity of methane emission will be predicted by fitting mixed-effects models; prediction models will be developed using covariates such as GE intake, DMI, DMI and dietary neutral-detergent fiber (NDF), starch, or ether extract (i.e., fat) content, BW, average daily gain, etc. Global (all data in the database) or regional (i.e., Europe, North America, South America/Brazil, etc.) models will be developed. The Bayesian information criterion (BIC) will be used as a model selection criterion. In addition, the presence of multicollinearity of fitted models will be examined based on the variance inflation factor (VIF). The largest VIF in excess of 5 among all predictor variables will be considered as an indicator of multicollinearity. The predictive accuracy of the developed methane prediction models will be evaluated using a leave-one-out cross-validation (e.g., James et al., 2014). Models developed in each category will be evaluated using a combination of model evaluation metrics [e.g., mean square prediction error (RMSPE), decomposed into mean bias, slope bias, and random bias; square root of the RMSPE, used to assess overall model prediction error; the RMSPE-observations standard deviation ratio; and concordance correlation coefficient].An approach, similar to the BEEF database analysis, will be used to process the SMALL RUMINANTS database, which currently has 2,875 individual animal data from Europe, Asia, North America, Australia, and New Zealand.Enteric Methane Mitigation (EMM) database: the development of the EMM database is currently underway in the GLOBAL NETWORK project. The studies in the database will contain multiple treatment groups sharing a common control group; hence, the effect size estimates will not be statistically independent. Therefore, a robust variance estimation (RVE) method (Tanner-Smith et al., 2016) will be used to analyze statistically dependent effect sizes. To understand possible causes of the heterogeneity in responses, individual explanatory variables such as measurement technique, dietary nutrient content, BW, DMI, etc., will be evaluated in RVE mixed effects meta-regression models. Analyses will be conducted separately for treatment type and response variable combination for all animal species (cattle, buffalo, sheep, goat, and alpaca) and for cattle alone (which is the largest animal species group in the database). Computations will be carried out using the metafor (version 2.0-0) and robumeta (version 2.0) packages in R (version 3.1.1, R Foundation for Statistical Computing, Vienna, Austria).The intention of the GLOBAL NETWORK team is to have the EMM database available to the global animal science community for continuous updating. Dr. Hristov is currently discussing the possibility of hosting the database at the Global Research Alliance's website. When this project is completed, the EMM database will be an invaluable resource for stakeholders of continuously updated information on the effectiveness of enteric methane mitigation practices. A similar model will be followed for the NEM (see below), once it is completed.Objective 2: A format similar to the EMP database work will be followed for developing the Nitrogen Emission Prediction (NEP) database. This work is currently in its initial stages under the GLOBAL NETWORK project. The NEP database is divided into DAIRY and BEEF databases. The DAIRY sub-database has accumulated approx. 2,500 nitrogen utilization-related individual animal observations, including DMI, diet composition, and nitrogen excretion and secretion data. The BEEF sub-database has about 600 equivalent observations. We expect that, through the GLOBAL NETWORK and CEDERS partners and our FNN contacts, we will be able to generate NEP databases for dairy and beef cattle and small ruminants of similar size to the EMP [i.e., from about 2,000 (beef and small ruminants) to over 6,000 (dairy) individual animal observations]. Processing and analysis of the NEP databases will be conducted in a manner similar to the EMP database described under Objective 1 above.The format and statistical analysis for the Nitrogen Emission Mitigation (NEM) database will be similar to the EMM database (Objective 1). Exiting in vivo studies published in peer-reviewed journals in English, French, and German and unpublished data from GLOBAL NETWORK and CEDERS project members will be assembled and analyzed to offer recommendations to stakeholders for most efficient and technically feasible mitigation practices for nitrate, ammonia, and nitrous oxide emissions from ruminant livestock systems.Objective 3: One of the main goals of this project is to ensure continuous involvement of U.S. scientists in important on-going international activities, such as the CEDERS (Capturing Effects of Diet on Emissions from Ruminant Systems) project. The GLOBAL NETWORK project, led by Dr. Hristov, was the foundation for CEDERS; however, U.S. scientists are not currently involved in CEDERS.The CEDERS team expects the following results from the project: (1) Refinement of methodologies to estimate the effect of dietary measures on different on-farm GHG emissions, and their trade-offs; (2) Identify relationships between dietary factors and GHG emissions working in conjunction with the FNN under the GRA platform, and expand on current databases from the FACCE JPI GLOBAL NETWORK, U.K. GHG Platform projects and the French GHG database, and other available national data or databases; (3) Identify relationships between dietary factors and whole farm GHG profile, as well as the emission potential of enteric methane and emissions from excreta/manure; (4) Provide refined insight into the inter-dependencies of on-farm GHG emissions (trade-offs and synergies, integral assessments), and the consequences for farm- and country-specific methodologies of GHG accounting; (5) Provide country and end-user specific recommendations on how to capture and account for the consequences of dietary measures on GHG emissions with on-farm accounting tools and national GHG inventory methodologies; and (6) Provide guidelines for non-partner countries (regions other than the EU and NZ) that seek collaboration and consider the adoption of GHG accounting methodologies, on capturing the effect of dietary measures on GHG emissions in on-farm accounting tools and national GHG inventories.

Progress 04/15/19 to 02/01/24

Outputs
Target Audience:Animal and Environmental scientists, Animal Science undergraduate and graduate students, Extension, General public. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Continuous interaction with an international team of scientists with diverse background and expertise. Attendance of scientific meeting such as GGAA and other. How have the results been disseminated to communities of interest?Publication of research articles listed in this and previous reports. What do you plan to do during the next reporting period to accomplish the goals?The project expired.

Impacts
What was accomplished under these goals? One of the projects completed under this grant intended to develop CH4 prediction equations for beef cattle and had the following objectives: (1) predict CH4 production, yield and intensity using an intercontinental database (data from Europe, North America, Brazil, Australia and South Korea); and (2) assess the impact of geographic region, and of higher- and lower-forage diets. Europe- and North America-specific subsets predicted by the best performing ≥25% forage multiple regression equation had RMSPE of 24.5 and 20.4%, whereas these errors were 24.5 and 20.0% with region-specific equations, respectively. Predicting beef cattle CH4 production using energy conversion factors, as applied by the Intergovernmental Panel on Climate Change, indicated that adequate forage content-based and region-specific energy conversion factors improve prediction accuracy and are preferred in national or global inventories. Another project aimed at developing empirical models to predict CH4 emissions from sheep had the following objectives: (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 developed under the project included 2,135 individual animal data from 13 countries. The study developed prediction models for sheep CH4 production based on DMI and concluded that: (1) prediction accuracy will improve national and global inventories if separate equations for young and adult sheep are used with the additional variables body weight BW, organic matter digestibility and rumen propionate proportion; and (2) appropriate universal equations can be used to predict CH4 production from sheep across different diets and climatic conditions. A meta-analysis identified strategies to decrease product-based (PB; CH4 per unit meat or milk) and absolute (ABS) enteric CH4 emissions while maintaining or increasing animal productivity (AP; weight gain or milk yield). Next, the potential of different adoption rates of one PB or one ABS strategy to contribute to the 1.5°C climate target was estimated. The database included findings from 430 peer-reviewed studies, which reported 98 mitigation strategies that can be classified into three categories: animal and feed management, diet formulation, and rumen manipulation. Three PB strategies - namely, increasing feeding level, decreasing grass maturity, and decreasing dietary forage-to-concentrate ratio - decreased CH4 per unit meat or milk by on average 12% and increased AP by a median of 17%. Five ABS strategies - namely CH4 inhibitors, tanniferous forages, electron sinks, oils and fats, and oilseeds--decreased daily methane by on average 21%. Globally, only 100% adoption of the most effective PB and ABS strategies can meet the 1.5°C target by 2030 but not 2050, because mitigation effects are offset by projected increases in CH4 due to increasing milk and meat demand. Notably, by 2030 and 2050, low- and middle-income countries may not meet their contribution to the 1.5°C target for this same reason, whereas high-income countries could meet their contributions due to only a minor projected increase in enteric CH4 emissions. Another project aimed collate databases of individual dairy and beef cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region. The best-developed CH4 production models from the study outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries. A similar effort was undertaken with beef data from the LAC region. The developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data. Similar efforts were made with a sheep database from the LAC region (not reported here due to space limitation). Another database for the LAC region aimed at identifying feasible enteric CH4 mitigation strategies, which do not compromise animal performance. After outlier's removal, 2745 animal records (65% of the original data) from 103 studies were retained (from 2011 to 2021) in the database. Six strategies decreased at least one enteric CH4 metric and simultaneously increased milk yield (MY; dairy cattle) or average daily gain (ADG; beef cattle and sheep). The breed composition F1 Holstein × Gyr decreased CH4 emission per MY (CH4IMilk) while increasing MY by 99%. Adequate strategies of grazing management under continuous and rotational stocking decreased CH4 emission per ADG (CH4IGain) by 22 and 35%, while increasing ADG by 22 and 71%, respectively. Increased dietary protein concentration, and increased concentrate level through cottonseed meal inclusion, decreased CH4IMilk and CH4IGain by 10 and 20% and increased MY and ADG by 12 and 31%, respectively. Lastly, increased feeding level decreased CH4IGain by 37%, while increasing ADG by 171%. The identified effective mitigation strategies can be adopted by livestock producers according to their specific needs and aid LAC countries in achieving SDG as defined in the Paris Agreement. A database specific to the Southeast Asia (SE-Asia) region was also developed to model enteric CH4 emissions from ruminants from that region. The study provided improved CH4 prediction models for beef cattle managed under diverse feeding systems in SE-Asia and affirmed that region-specific models are needed to reliably predict beef cattle CH4 emission at national or regional levels, particularly for low- and middle-income countries. The GLOBAL NETWORK team also developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using statistical/machine learning (ML) methods. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/ animal/d) and CH4 yield (g CH4/ kg of dry matter intake). The study highlighted the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, the methods used facilitated the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

Publications

  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Zhang, B., L. Moraes, J. Firkins, Z. Yu, A. N. Hristov, E. Kebreab, P. H. Janssen, A. Bannink, A. R. Bayat, L. A. Crompton, J. Dijkstra, M. A. Eug�ne, M. Kreuzer, M. McGee, C. K. Reynolds, A. Schwarm, K. J. Shingfield, and D. R. Y��ez-Ruiz. 2023. Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy. Scientific Reports 13:21305. https://doi.org/10.1038/s41598-023-48449-y
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: Hristov, A. N. 2023. Perspective: Could dairy cow nutrition meaningfully reduce the carbon footprint of milk production? J. Dairy Sci. 106:73367340. https://doi.org/10.3168/jds.2023-23461
  • Type: Journal Articles Status: Under Review Year Published: 2024 Citation: Kjeldsen, M. H, M. Johansen, M. R. Weisbjerg, A.L.F. Hellwing, A. Bannink, S. Colombini, L. Crompton, J. Dijkstra, M. Eug�ne, A. N. Hristov, P. Huhtanen, A. Jonker, M. Kreuzer, B. Kuhla, C. Martin, P. J. Moate, M. Niu, N. Peiren, C. Reynolds, S. R. O. Williams, and P. Lund. 2024. Predicting CO2 production from lactating dairy cows based on animal and production traits obtained from an international dataset. J. Dairy Sci.
  • Type: Journal Articles Status: Accepted Year Published: 2023 Citation: Martins, L. F., S. F. Cueva, C. F. A. Lage, M. Ramin, T. Silvestre, J. Tricarico, and A. N. Hristov. 2023. A meta-analysis of enteric methane mitigation effect of strategies evaluated in vitro. J. Dairy Sci. 107. https://doi.org/10.3168/jds.2023-23419
  • Type: Journal Articles Status: Published Year Published: 2023 Citation: de Ondarza, M. B., A. N. Hristov, and J. M. Tricarico. 2023. A global dataset of enteric methane mitigation experiments with lactating and non-lactating dairy cows conducted from 1963 to 2022. Data in Brief. https://doi.org/10.1016/j.dib.2023.109459


Progress 04/15/22 to 04/14/23

Outputs
Target Audience:Animal and Environmental scientists, Animal Science undergraduate and graduate students, Extension, General public. Changes/Problems:Database and publication work was delayed due to COVID-19 and lack of personnel to process the data. We have requested a no-cost extension of the project and plan to complete it by the new end date. What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?Presentations at scientific meetings and peer-reviewed journal publications. What do you plan to do during the next reporting period to accomplish the goals?(Objective 1) Finalize the analysis of the nitrogen excretion database for the Latin America and the Caribbean (LAC) region; (Objective 2) Publish a manuscript from the LAC Nitrogen database; (Objective 3) Complete the analysis of a rumen microbiome database designed to predicting enteric methane emissions from small ruminants; and (Objective 4) Publish a manuscript from the microbiome database developed under Objective 3.

Impacts
What was accomplished under these goals? During 2022, we completed the preparation and publication of analyses of several databases that were delayed due to COVID-19 and lack of personnel to process the data. These projects include publications that are highlighted in this report.

Publications

  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Alejandro Belanchea, A. N. Hristov, Henk J. van Lingend, Stuart E. Denmane, Ermias Kebreabf, Angela Schwarmg, Michael Kreuzerh, Maguy Eug�nei, Vincent, Niderkorni, C�cile Martini, Harry Archim�dej, Mark McGeek, Christopher K. Reynolds, Les A. Cromptonl, Ali-Reza Bayatm, Zhongtang Yu n, Andr� Banninko, Jan Dijkstrap, Alex V., Chavesq, Harry Clarkr, Stefan Muetzels, Vibeke Lindt, Jon Moorbyu, John A. Rookev, Walter Antezanaw, Mi Wangw, Roger Hegartyy, Jean V�ctor Savianz, Adibe Luiz Abdallaaa, Yosra A. Soltanab, Alda L�cia Gomes Monteiroac, Juan Carlos Ku-Veraad, Gustavo Jaurenaae, Carlos A. G�mez-Bravoaf, Olga L. Mayorgaag, Guilhermo de Souzaah and David R. Y��ez-Ruiz. 2022. Prediction of enteric methane emissions by sheep using an intercontinental database. Journal of Cleaner Production 384:135523. https://doi.org/10.1016/j.jclepro.2022.135523
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Congio, G.F.S., Bannink, A., Mayorga, O.L., Rodrigues, J.P.P., Bougouin, A., Kebreab, E., Carvalho, P.C.F., Berchielli, T.T., Mercadante, M.E.Z., Valadares-Filho, S.C., Borges, A.L.C.C., Berndt, A., Rodrigues, P.H.M., Ku-Vera, J.C., Molina-Botero, I.C., Arango, J., Reis, R.A., Posada-Ochoa, S.L., Pereira, L.G.R., Castel�n-Ortega, O.A., Marcondes, M.I., G�mez, C., Ribeiro-Filho, H.M.N., Gere, J.I., Ariza-Nieto, C., Giraldo, L.A., Gonda, H., Cer�n-Cucchi, M.E., Hern�ndez, O., Ricci, P., Hristov, A.N., 2022. Prediction of enteric methane production and yield in beef cattle using a Latin America and Caribbean database. STOTEN, Science of the Total Environment 856:159128. http://dx.doi.org/10.1016/j.scitotenv.2022.159128
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Congio, G. F. S., A. Bannink, O. L. Mayorga, J. P. P. Rodrigues, A. Bougouin, E. Kebreab, P. C. F. Carvalho, A. L. Abdalla, A. L. G. Monteiro, J. C. Ku-Vera, J. I. Gere, C. Gomez , and A. N. Hristov. 2022. Prediction of enteric methane production and yield in sheep using a Latin America and Caribbean database. Livest. Sci. 264: 105036 https://doi.org/10.1016/j.livsci.2022.105036
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Tee, T. P., Y. M. Goh, M. H. M. Zainudin, S. C. L. Candyrine, K. Sommart, K. Kongphitee, W. Sumamal, I. Phaowphaisal, R. Namsilee, W. Angthong, S. Sunato, O. Keaokliang, K. Maeda, N. V. Thu, T. T. Trung, N. T. K. Dong, A. Purnomoadi, M. Kurihara, A. Jayanegara, K. Higuchi, Y. Kobayashi, F. Ohtani, H. Abe, F. Terada, H. Kumagai, H. Matsuyama, I. Nonaka, N. Takusari, N. Shiba, K. Hosoda, T. Suzuki, Y. Kamiya, T. Nishida, K. Hayasaka, M. Shibata, M. Wang, Z. L. Tan, R. Wang, E. Kebreab, H. J. van Lingen, A. N. Hristov, and J. B. Liang. 2022. Enteric methane emission models for diverse beef cattle feeding systems in South-east Asia: A meta-analysis. Anim. Feed Sci. Technol. 294:115474. https://doi.org/10.1016/j.anifeedsci.2022.115474


Progress 04/15/21 to 04/14/22

Outputs
Target Audience:Animal and Environmental scientists, Animal Science undergraduate and graduate students, Extension, General public Changes/Problems:Due to COVID-19, some project activities were not completed as planned and will require additional time. Therefore, we will be requesting a one-year no-cost extension (through end of April 2023). What opportunities for training and professional development has the project provided?Attendance of scientific meeting and networking with colleagues around the world. How have the results been disseminated to communities of interest?Publications and Scientific Presentations. What do you plan to do during the next reporting period to accomplish the goals?Continue working on the beef cattle nitrogen prediction database and manuscript. We also need to publish the manuscript from the sheep and small ruminants database and the microbial database. Due to COVID-19, these activities were not completed as planned and will require additional time. The US team will also participate in preparation of manuscripts originating from the CEDERS project, which was also delayed due to CIVID-19 lockdowns.

Impacts
What was accomplished under these goals? Goal 1. The dairy and beef EMP databases were completed and manuscripts published. The small ruminants database is also completed and is currently being analyzed. We expect to have a manuscript from it published sometime in 2022. In addition, the EMM database work was also completed and a manuscript was submitted to PNAS. The review of that manuscript is currently in its final stages. Another EMM database was developed for the Latin America and Caribbeans region and a manuscript from it was also published. An EMM database for the Southeast Asia region was also developed and a manuscript written and submitted for publication. Goal 2. The dairy nitrogen database has been completed and a manuscript was submitted for publication to J. Dairy Sci. This manuscript is in its final review stage; we expect it to be published in 2022. Prediction models developed using this database are being used to update USDA's 2014 report on "Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry: Methods for Entity-Scale Inventory". Goal 3. Continue collaboration with the CEDERS team on updating manure GHG emission prediction models based on nutritional factors.

Publications

  • Type: Journal Articles Status: Accepted Year Published: 2022 Citation: 4. Arndt, C., A. N. Hristov, W. J. Price, S. C. McClelland, A. M. Pelaez, S. F. Cueva, J. Oh, A. Bannink, A. R. Bayat, L. A. Crompton, J. Dijkstra, M. A. Eug�ne, D. Enahoro, E. Kebreab, M. Kreuzer, M. McGee, C. Martin, C. J. Newbold, C. K. Reynolds, A. Schwarm, K. J. Shingfield, J. B. Veneman, D. R. Y��ez-Ruiz, and Z. Yu. 2022. Full Adoption of Strategies to Mitigate Enteric Methane Emissions by Ruminants and How They Can Help to Meet the 1.5�C Climate Target by 2030 but Not 2050. Proc. Nat. Acad. Sci. U.S.A. (accepted).
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 14. Congio, G. F. S., A. Bannink, O. L. M. Mogoll�n, Latin America Methane Project Collaborators, and A.N. Hristov. 2021. Enteric methane mitigation strategies for ruminant livestock systems in the Latin America and Caribbean region: A Meta-Analysis. J. Cleaner Prod. https://doi.org/10.1016/j.jclepro.2021.127693
  • Type: Journal Articles Status: Accepted Year Published: 2022 Citation: 2. Congio, G.F.S., Bannink, A., Mayorga, O.L., Rodrigues, J.P.P., Bougouin, A., Kebreab, E., Silva, R.R., Maur�cio, R.M., da Silva, S.C., Oliveira, P.P.A., Mu�oz, C., Pereira, L.G.R., G�mez, C., Ariza-Nieto, C., Ribeiro-Filho, H.M.N., Castel�n-Ortega, O.A., Rosero-Noguera, J.R., Paz, M., Rodrigues, P.H.M., Marcondes, M.I., Astigarraga, L., Abarca, S., Hristov, A.N., 2022. Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database. Science of the Total Environment (accepted).


Progress 04/15/20 to 04/14/21

Outputs
Target Audience:Animal and Environmental scientists, Animal Sceince undergraduate and graduate students, Extension, General public 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?Publications and Scientific Presentations What do you plan to do during the next reporting period to accomplish the goals?Continue working on the nitrogen prediction database and manuscript. We also need to publish the manuscript from the treatment mean database and sheep and small ruminants database.

Impacts
What was accomplished under these goals? Goal 1. The beef database was completed and a manuscript published. The small ruminants database is also completed and is currently being analyzed. We expect to have a manusript from it published sometime in 2021. Goal 2. The nitrogen database has also been completed and a manuscript draft was submitted to co-authors for internal review. We expect the paper from this part of the project to be published in 2021. Prediction models developed using this database are being used to update USDA's 2014 report on "Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry: Methods for Entity-Scale Inventory". Goal 3. Continue collaborattion with the CEDERS team on updating manure GHG emission prediction models based on nutritional factors.

Publications

  • Type: Journal Articles Status: Under Review Year Published: 2021 Citation: 5. Benaouda, M., C. Martin, C. Arndt, A. Bannink, A. R. Bayat, L. A. Crompton, J. Dijkstra, S. van Gastelen, M. McGee, A. L. F. Hellwing, A. N. Hristov, P. Huhtanen, A. Jonker, H. van Lingen, C. de Klein, M. Kreuzer, D. Krol, B. Kuhla, G. Lanigan, P. Lund, S. C. McClelland, P. J. Moate, C. Munoz, J. Oh, N. Peiren, M. Ramin, C. K. Reynolds, A. Schwarm, K. J. Shingfield, M. R. Weisbjerg, D. R. Y��ez-Ruiz, Z. Yu, E. Kebreab, and M. Eug�ne. XXXX. Assessment of nutritional strategies to mitigate enteric methane emissions of ruminant and their relation with manure composition. J. Cleaner Production (submitted).
  • Type: Journal Articles Status: Submitted Year Published: 2021 Citation: 6. Zhang, B., L. Moraes, J. Firkins, Z. Yu, A. N. Hristov, E. Kebreab, P. H. Janssen, A. Bannink, A. R. Bayat, L. A. Crompton, J. Dijkstra, M. A. Eug�ne, M. Kreuzer, M. McGee, C. K. Reynolds, A. Schwarm, K. J. Shingfield, and D. R. Y��ez-Ruiz. XXXX. Prediction of methane emission from sheep using data of rumen microbiota. Global Change Biology (submitted).


Progress 04/15/19 to 04/14/20

Outputs
Target Audience:Animal and Environmental scientists, Animal Sceince undergraduate and graduate students, Extension, General public. Changes/Problems:No major changes. What opportunities for training and professional development has the project provided?Particpating in GGAA meeting in Brazil in August 2019 allowing for networking and collaborative activities. Particpation in an initiation workshop of the Southeast Asia methane project; Hristov was invited to talk about the Global Network project and the Latin America Mathane project and share data collection templates with SEA researchers. How have the results been disseminated to communities of interest?Presnetaion at the GGAA meeting (Arndt, C., A. N. Hristov, S. C. McClelland, W. Price, E. Kebreab, J. Oh, A. Bannink, A. R. Bayat, L. A. Crompton, J. Dijkstra, M. A. Eugène, M. Kreuzer, M. McGee, C. K. Reynolds, A. Schwarm, K. J. Shingfield, D. R. Yáñez-Ruiz, Z. Yu, J. B. Veneman, and C. J. Newbold. 2019. Promising Nutritional Strategies to Reduce Enteric Methane Emissions. Pages 147-148, In Berndt A., Pereira Ribeiro, L. G., and Abdala, A. L. (Eds.). Proceedings of the 7th International Greenhouse Gas and Animal Agriculture Conference. Iguassu Falls, Brazil, Embrapa Southeast Livestock, São Carlos, SP) and a publication in a scientific journal (van Lingen, H. J., Mutian Niu, Ermias Kebreab, Sebastião C. Valadares Filho, John A. Rooke, Angela Schwarm, Michael Kreuzer, Phil I. Hynd, Mariana Caetano, Maguy Eugène, Cecile Martin, Mark McGee, Padraig O'Kiely, Martin Hünerberg, 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. Yáñez-Ruiz, Zhongtang Yu, André 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 283:10657). What do you plan to do during the next reporting period to accomplish the goals?We will continue working on the Small Ruminants database, the Nitrogen databases, and contnue our collaborations with the CEDERS project team.

Impacts
What was accomplished under these goals? 1. Under Objective 1, we have published the beef methane database (van Lingen, H. J., Mutian Niu, Ermias Kebreab, Sebastião C. Valadares Filho, John A. Rooke, Angela Schwarm, Michael Kreuzer, Phil I. Hynd, Mariana Caetano, Maguy Eugène, Cecile Martin, Mark McGee, Padraig O'Kiely, Martin Hünerberg, 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. Yáñez-Ruiz, Zhongtang Yu, André 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 283:106575). The Enteric Methane Mitigation database is almost complete. Report on progress was presented at the GGAA meeting in Brazil (Arndt, C., A. N. Hristov, S. C. McClelland, W. Price, E. Kebreab, J. Oh, A. Bannink, A. R. Bayat, L. A. Crompton, J. Dijkstra, M. A. Eugène, M. Kreuzer, M. McGee, C. K. Reynolds, A. Schwarm, K. J. Shingfield, D. R. Yáñez-Ruiz, Z. Yu, J. B. Veneman, and C. J. Newbold. 2019. Promising Nutritional Strategies to Reduce Enteric Methane Emissions. Pages 147-148, In Berndt A., Pereira Ribeiro, L. G., and Abdala, A. L. (Eds.). Proceedings of the 7th International Greenhouse Gas and Animal Agriculture Conference. Iguassu Falls, Brazil, Embrapa Southeast Livestock, São Carlos, SP). The small ruminant database is being currently analyzed. It is a global database and includes 2,086 individual animal observations from Oceania, Europe, Souith and North America, and Asia. We expect that the full paper for this database will be published in 2020. 2. Under Objective 2, we have completed collection of individual animal data for developing the Nitrogen Emission Prediction database. Currently, there are over 6,000 observations in the database. The database is being analyzed by our group and we expect a preliminary publication in 2020. Work on the Nitrogen Emission MItigation database has not started yet. 3. Under Objective 3, we have been closely working with the CEDERS team of scientists and specifically, Andre Bannink and Pekka Huhtanen. We (the Feed and Nutrition Network, the Global Network Project, and CEDERS) held a joint workshop as part of the GGAA meeting in Brazil in August 2019. The workshop was a complete sucess with over 60 partiicpants from all over the world, exchanging ideas, data, and plans for furture collaboration. Future collaboration with CEDERS will involve the Nitrogen Emission Prediction database, which also contains manure emission data, as part of CEDERS objectives.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Arndt, C., A. N. Hristov, S. C. McClelland, W. Price, E. Kebreab, J. Oh, A. Bannink, A. R. Bayat, L. A. Crompton, J. Dijkstra, M. A. Eug�ne, M. Kreuzer, M. McGee, C. K. Reynolds, A. Schwarm, K. J. Shingfield, D. R. Y��ez-Ruiz, Z. Yu, J. B. Veneman, and C. J. Newbold. 2019. Promising Nutritional Strategies to Reduce Enteric Methane Emissions. Pages 147-148, In Berndt A., Pereira Ribeiro, L. G., and Abdala, A. L. (Eds.). Proceedings of the 7th International Greenhouse Gas and Animal Agriculture Conference. Iguassu Falls, Brazil, Embrapa Southeast Livestock, S�o Carlos, SP.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: 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 283:106575.