, Sequence results were aligned using DNA Star and compared using BLAST program (NCBI) and EzTaxon. Results showed that numbers of bacterial isolates were comparatively higher in Korean native goats (8 spp.) than Holstein cows (3 spp.). Nine bacterial species belonging to Firmicutes are potential fumarate reducing bacteria which were identified in this study, These bacteria showed high homology with Bacillus licheniformis ATCC 14580T (CP000002), Bacillus smithii NBRC 15311T (AB271749), Bacillus subtilis subsp. Inaquosorum BGSC 3A28T (EU138467), Bacillus thermoamylovorans CNCM I-1378T (L27478), Clostridium bifermentans ATCC 638T (AB075769), Clostridium cochlearium ATCC 17787T (AB538429), Clostridium glycolicum DSM 1288T (X76750), Lactobacillus johnsonii ATCC 33200T (ACGR01000047) and Mitsuokella jalaludinii M9 DSM 13811T (AF479674). reduction. Furthermore, the isolated bacteria will be
, Evaluation of rumen methanogen diversity with diets containing corn dry distillers grains with solubles and condensed tannins using PCR-DGGE and qRT-PCR analysis, R16
, All diets contained 8% barley silage with CDDGS and CT included by replacing an equivalent amount of barley grain from the Ctrl diet. Rumen digesta samples were collected before feeding on d 25 and d 28 from dorsal, ventral, cranial and caudal aspects of the rumen. Total DNA was extracted from the rumen digesta samples pooled by cows for each period. The PCR-products obtained by amplification with universal methanogen primers were subjected to PCR-DGGE analysis. Similarity of the DGGE profiles was determined using Dice's coefficient at 0.5% optimization and 0.625% tolerance. Bands corresponding to Methanosphaera stadmanae and Methanbrevibacter species were identified. Although no significant diet effect on total methanogen profiles was observed, some bands were found to be associated with Ctrl (bands 26, 36 and 43) and CDDGS diets, Condensed tannins (CT) and distillers' grains from ethanol production have the potential to reduce greenhouse gases from ruminants. However, effects on methanogen diversity in cattle have not been investigated, vol.17, p.31
, Impact of cold acclimatization on enteric methane emissions of beef cows fed protein deficient and sufficient forage-based diets, R34
, These diets were fed in thermal neutral (fall) and cold-stressed (winter) environments, with average, minimum and maximum temperatures of 7.3, 2.7 and 13.8, and -17.7, -23.5 and -11.0ºC in fall and winter, respectively. Dry matter intake was not significantly different between diets or seasons. Available CP intake (ACPI), however, was significantly different (P<0.0001) between diets with 576.7, 918.9 and 1349.6 g d -1 for control, 10% and 20% DDGS diets respectively, but did not differ between seasons. As expected, serum urea nitrogen (SUN) and winter, respectively. As a result of increasing dietary CP content, enteric CH 4 emissions expressed as a percent of gross energy intake
, 3% GEI for control, 10% and 20% DDGS diets, respectively. When cold acclimatized, cows had significantly
, Sixteen case studies were examined that included Merino fine wool, prime lamb, cow/calf, steer, dairy, wheat and canola systems, with each represented firstly as an average farm and then as a top-producing farm. The aim was to develop a baseline of emissions produced by these enterprises and to examine farm emissions in relation to the amount of produce such as wool, meat, milk solids or grains. Biophysical models GrassGro and DairyMod were used to simulate the livestock systems and the models' outputs were then fed into an emissions calculator. This calculator used a yearly time frame and employed the International Panel on Climate Change methodology, as currently used in the Australian inventory. The calculator included CH 4 and N 2 O on-farm emissions but excluded energy and transport emissions, which are not defined as agricultural emissions in the National Inventory. The dairy farms produced the highest emissions per hectare (6.3-7.6 t CO 2 -e ha -1 ), followed by beef (3.4-5.2 t CO 2 -e ha -1 ), sheep (2.8-4.3 t CO 2 -e h -1 ) and lastly grains (0.2 t CO 2 -e ha -1 ). When compared on an emissions intensity basis, wool emitted the most of all enterprises, Agriculture in Australia contributes 16% of national greenhouse gas emissions, with enteric methane and nitrous oxide contributing 10.4 and 2.8% of national emissions, respectively, pp.61-68
, The profile of milk fatty acids (FA) may be related to methane production, given that diet composition affects rumen microbial metabolism and methanogenesis, as well as the supply of preformed FA and FA precursors to the mammary gland. The aim of this experiment was to evaluate the relationship of milk FA profile with methane production in dairy cattle. Data from three experiments with dairy cattle, encompassing 10 dietary treatments and 50 observations, were used. Dietary treatments included supplementation with calcium fumarate, diallyldisulfide, caprylic acid, capric acid, lauric acid, myristic acid, extruded linseed, linseed oil, and yucca powder, Milk C10:0, C11:0, C14:0iso, C15:0iso, and C16:0 were positively related (P<0.05) to methane (g/kg DMI), whereas C17:0iso, C17:0anteiso
, after correction for experiment effect; parameters significant at P<0.02). The present analysis confirmed the expected positive relationship between methane and C14:0iso and C15:0iso in milk FA, as well as the negative relationship between methane and various trans-intermediates
, Impacts of future climate scenarios on nitrous oxide emissions from pasture-based dairy systems in south eastern Australia, R46
, Total pasture N inputs can be in excess of 300 kg N ha -1 yr -1 , with N surpluses >200 kg N ha -1 yr -1 , resulting in high emissions of N 2 O. Climate change scenarios for south eastern Australia suggest increasing temperatures, declining rainfall and longer dry summer seasons, raising the question of the potential impact of future climate change scenarios on N 2 O emissions. The effect of four future climate change scenarios (measured baseline of 1971-2000, A1FI 2030 high emission scenario and 2070 mid (A1B) and high (A1FI) emission scenario) on N 2 O emissions were modelled, for four differing soil types, climates and systems (Elliott, Terang, Ellinbank and R48. Uncertainties and variation in carbon footprint for milk production in Sweden estimated by Monte Carlo simulation, O) emissions account for c. 10 % of global GHG emissions, with the vast majority of these emissions (c. 90%) derived from agriculture
, J Dairy Sci, vol.90, pp.3456-3467, 2007.
, Methane (kJ kg -1 feed DM) = 1802 -21.1 × DMI (g kg -1 body weight, vol.61, pp.10-19, 2007.
, J. Anim. Sci, vol.81, pp.3141-3150, 2003.
, Cows were given common diets between weaning and wintering, and precalving (from February until turn-out). The three diets in the DL were hay (HY), barley silage/oat straw (SS; 40:60, dry matter (DM) basis) and barley grain/oat straw (BS; 30:70 DM basis). Data were subjected to COWPOLL, MOLLY, IPCC Tier 2 and an empirical model (Ellis et al. 2009) to rank the different production systems according to the estimate of EME over 90 d. The EME factors obtained from the mechanistic models (COWPOLL and MOLLY) varied from 5.66 to 8.62% of GEI, compared to a fixed EME factor for beef cows of 6.5% of Gross Energy Intake (GEI) recommended by IPCC. The EME estimated for AG and G were similar, although the AG had slightly higher values, because of a higher estimated feed intake. Rankings of EME by the mechanistic models, which took in detailed information on fibre, starch, N and volatile fatty acids, were the same for both feeding periods. The empirical model (based on the starch-to-ADF ratio and feed intake) determined lower volume of methane produced, but the ranking of feeding systems by EME was similar to results obtained from the mechanistic models. All the models except IPCC Tier 2 ranked the EME for winter systems from lowest to highest as SS, BS, EG and HY. The mechanistic models kept the same ranking when EME was calculated per kg of cow body weight maintained. The SS and BS, Canada contributes to the national pool of enteric methane emissions (EME)
, The quantification of greenhouse gas and ammonia emissions is carried out with the CAPRI model. CAPRI is a global economic model for agriculture with a regionalized focus for Europe. Its database comprises time series of mutually consistent national and regional data and covers market balances at the national scale. The quantification of methane emissions from enteric fermentation and manure management follows the IPCC 2006 guidelines, a Tier 2 approach for cattle activities and a Tier 1 approach for swine, poultry, sheep and goats. Nitrogen emissions are calculated according to a mass flow approach of the MITERRA-EUROPE model. It considers emissions sources as defined in the IPCC guidelines. Furthermore, we calculate GHG emissions from on-farm energy use including indirect emissions from machinery and buildings, pesticide usage and emissions from fertilizer production. Emissions from imported feeds are accounted for as well as emissions occurring during the transport of feed. Finally, we consider emissions from global land use changes induced by EU livestock production, It is well established that significant amounts of greenhouse gases (GHG) are emitted from the processes involved in animal husbandry production systems. However, GHG mitigation strategies often focus on a single gas in isolation. Ultimately, it is essential that reducing emissions of one GHG does not result in increases in other GHG in different areas of the farm system, 2010.
, Two important features of the model are that it uses inputs obtained easily from farmers, and it can model effects of a range of management and mitigation practices. The model is used extensively within New Zealand and is central to each farm producing a nutrient management plan. It is also increasingly being used as a tool in the implementation of regulatory policies for controlling N leaching and forms the basis for a cap and trade policy mechanism to control N leaching to Lake Taupo in Central North Island. Overseer is considered within NZ as a possible vehicle for calculating farm-specific greenhouse gas (GHG) emissions if a farm-specific trading scheme is adopted. Therefore, the role of Overseer in underpinning water quality policy serves as a useful case study in the operational challenges of using a farmscale model in an emissions trading scheme. The farm-specific estimates of GHG emissions requires inputs of (i) animal numbers and types and their diet to estimate CH 4 emissions, (ii) fertiliser inputs and excreta urine to