Suchergebnisse

Mitigation of greenhouse gas emissions in livestock production - A review of technical options for non-CO2 emissions -- Contents -- Editors' preface -- Abstract -- Abbreviations and acronyms -- Introduction -- Livestock global non-CO2 greenhouse gas emissions -- Scope and approach -- Mitigation practices -- Enteric fermentation -- Manure and manure management -- Animal husbandry -- Species summaries by management system -- Interactions among mitigation practices -- Interactions in the rumen -- Interactions among feed additives, ration, enteric CH4 and animal productivity -- Interactions among feeding practices, manurestorage and land application -- Interactions among nutrition,animal health and productivity -- Appendix 1 - Enteric methane prediction equations -- Appendix 2 - Mitigation options summary table -- Bibliography.

Enteric methane (CH) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH emission conversion factors for specific regions are required to improve CH production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH yield and intensity prediction, information on milk yield and composition is required for better estimation. ; This study is part of the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE‐JPI)'s "GLOBAL NETWORK" project and the "Feeding and Nutrition Network" (http://animalscience.psu.edu/fnn) of the Livestock Research Group within the Global Research Alliance for Agricultural Greenhouse Gases (www.globalresearchalliance.org). Authors gratefully acknowledge funding for this project from: USDA National Institute of Food and Agriculture Grant no. 2014‐67003‐21979) University of California, Davis Sesnon Endowed Chair Program, USDA, and Austin Eugene Lyons Fellowship (University of California, Davis); Funding from USDA National Institute of Food and Agriculture Federal Appropriations under Project PEN 04539 and Accession number 1000803, DSM Nutritional Products (Basel, Switzerland), Pennsylvania Soybean Board (Harrisburg, PA, USA), Northeast Sustainable Agriculture Research and Education (Burlington, VT, USA), and PMI Nutritional Additives (Shoreview, MN, USA); the Ministry of Economic Affairs (the Netherlands; project BO‐20‐007‐006; Global Research Alliance on Agricultural Greenhouse Gases), the Product Board Animal Feed (Zoetermeer, the Netherlands) and the Dutch Dairy Board (Zoetermeer, the Netherlands); USDA National Institute of Food and Agriculture (Hatch Multistate NC‐1042 Project Number NH00616‐R; Project Accession Number 1001855) and the New Hampshire Agricultural Experiment Station (Durham, NH); French National Research Agency through the FACCE‐JPI program (ANR‐13‐JFAC‐0003‐01), Agricultural GHG Research Initiative for Ireland (AGRI‐I), Academy of Finland (No. 281337), Helsinki, Finland; Swiss Federal Office of Agriculture, Berne, Switzerland; the Department for Environment, Food and Rural Affairs (Defra; UK); Defra, the Scottish Government, DARD, and the Welsh Government as part of the UK's Agricultural GHG Research Platform projects (www.ghgplatform.org.uk); INIA (Spain, project MIT01‐GLOBALNET‐EEZ); German Federal Ministry of Food and Agriculture (BMBL) through the Federal Office for Agriculture and Food (BLE); Swedish Infrastructure for Ecosystem Science (SITES) at Röbäcksdalen Research Station; Comisión Nacional de Investigación Científica y Tecnológica, Fondo Nacional de Desarrollo Científico y Tecnológico (Grant Nos. 11110410 and 1151355) and Fondo Regional de Tecnología Agropecuaria (FTG/RF‐1028‐RG); European Commission through SMEthane (FP7‐SME‐262270). The authors are thankful to all colleagues who contributed data to the GLOBAL NETWORK project and especially thank Luis Moraes, Ranga Appuhamy, Henk van Lingen, James Fadel, and Roberto Sainz for their support on data analysis. All authors read and approved the final manuscript. The authors declare that they have no competing interests. ; Peer Reviewed