Search

Background: Panax notoginseng is a highly valued medicine and functional food, whose quality is considered to be influenced by the size, botanical parts, and growth environments. Methods: In this study, a HPLC method integrating fingerprinting and determination of multiconstituents by single reference standard was established and adopted to investigate the chemical profiles and active constituent contents of 215 notoginseng samples with different sizes, from different botanical parts and geographical regions. Results: Chemical differences among main root, branch root, and rotten root were not distinct, while rhizome and fibrous root could be discriminated from other parts. The notoginseng samples from Wenshan Autonomous Prefecture and cities nearby were similar, whereas samples from cities far away were not. The contents of major active constituents in main root did not correlate with the market price. Conclusion: This study provided comprehensive chemical evidence for the rational usage of different parts, sizes, and growth regions of notoginseng in practice. Keywords: chemical profile, geographical region, HPLC fingerprinting, multivariate analysis, Panax notoginseng

Enteric methane (CH) production attributable to beef cattle contributes to global greenhouse gas emissions. Reliably estimating this contribution requires extensive CH emission data from beef cattle under different management conditions worldwide. The objectives were to: 1) predict CH production (g d animal), yield [g (kg dry matter intake; DMI)] and intensity [g (kg average daily gain)] using an intercontinental database (data from Europe, North America, Brazil, Australia and South Korea); 2) assess the impact of geographic region, and of higher- and lower-forage diets. Linear models were developed by incrementally adding covariates. A K-fold cross-validation indicated that a CH production equation using only DMI that was fitted to all available data had a root mean square prediction error (RMSPE; % of observed mean) of 31.2%. Subsets containing data with ≥25% and ≤18% dietary forage contents had an RMSPE of 30.8 and 34.2%, with the all-data CH production equation, whereas these errors decreased to 29.3 and 28.4%, respectively, when using CH prediction equations fitted to these subsets. The RMSPE of the ≥25% forage subset further decreased to 24.7% when using multiple regression. 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. The developed equations had less RMSPE than extant equations evaluated for all data (22.5 vs. 23.2%), for higher-forage (21.2 vs. 23.1%), but not for the lower-forage subsets (28.4 vs. 27.9%). Splitting the dataset by forage content did not improve CH yield or intensity predictions. Predicting beef cattle CH 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., Authors gratefully acknowledge project funding from the USDA National Institute of Food and Agriculture Federal Appropriations under Project PEN 04539 and Accession number 1000803; 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); the Sesnon Endowed Chair program (UC Davis); the Swiss Federal Office of Agriculture, Berne, Switzerland; AHDB Beef and Lamb, the Scottish Government, Defra and the devolved administrations through the UK Agricultural Greenhouse Gas Inventory Research Platform; French National Research Agency through the FACCE-JPI program (ANR-13-JFAC-0003-01); the Cooperative Research Program for Agriculture Science, (Project No. PJ013448012018), RDA, Republic of Korea; the Australian Government Department of Agriculture, Fisheries and Forestry (Carbon Farming Futures Action on the Ground program; AOTGR2-0400); the financial support of the Reducing Emissions from Livestock Research Program, the National Livestock Methane Program, Meat and Livestock Australia, CSIRO and Ridley AgriProducts Pty, Ltd; the Institute of Science and Technology in Animal Science (INCTCA 465377/2014-9), the Department of Agriculture, Food and the Marine (DAFM), Ireland (AGRI-I project); European Commission through SMEthane (FP7‐SME‐262,270); Beef Cattle Research Council of the Canadian Cattlemen’s Association; the Cofund for Monitoring & Mitigation of Greenhouse Gases from Agri- and Silvi-culture (FACCE ERA-GAS)’s project Capturing Effects of Diet on Emissions from Ruminant Systems and the Dutch Ministry of Agriculture, Nature and Food Quality (AF-EU-18010 & BO-4400159-01).