Milk production, methane emissions, nitrogen, and energy balance of cows fed diets based on different forage systems

Evaluation of Intergovernmental Panel on Climate Change (IPCC) equations to predict enteric methane emission from lactating cows fed Mediterranean diets

The study aimed to evaluate Intergovernmental Panel on Climate Change (IPCC) Tier 2 (2006 and 2019) to predict enteric CH4 emissions from lactating cows fed Mediterranean diets. The effects of the CH4 conversion factor (Ym; CH4 energy loss as a percentage of gross energy intake) and digestible energy (DE) of the diet were evaluated as model predictors. A data set was created using individual observations derived from 3 in vivo studies on lactating dairy cows housed in respiration chambers and fed diets typical of the Mediterranean region based on silages and hays. Five models using different Ym and DE were evaluated following a Tier 2 approach: (1) average values of Ym (6.5%) and DE (70%) from IPCC (2006); (2) average value of Ym (5.7%) and DE (70.0%) from IPCC (2019; 1YM); (3) Ym = 5.7% and DE measured in vivo (1YMIV); (4) Ym = 5.7 or 6.0%, depending on dietary NDF, and DE = 70% (2YM); and (5) Ym = 5.7 or 6.0%, depending on dietary NDF, and DE measured in vivo (2YMIV). Finally, a Tier 2 model for Mediterranean diets (MED) was derived from the Italian data set (Ym = 5.58%; DE = 69.9% for silage-based diets and 64.8% for hay-based diets) and validated on an independent data set of cows fed Mediterranean diets. The most accurate models tested were 2YMIV, 2YM, and 1YMIV with predictions of 384, 377, and 377 (g of CH4/d), respectively, versus the in vivo value of 381. The most precise model was 1YM (slope bias = 1.88%; r = 0.63). Overall, 1YM showed the highest concordance correlation coefficient value (0.579), followed by 1YMIV (0.569). Cross-validation on an independent data set of cows fed Mediterranean diets (corn silage and alfalfa hay) resulted in concordance correlation coefficient of 0.492 and 0.485 for 1YM and MED, respectively. The prediction of MED (397) was more accurate than 1YM (405) in comparison with the corresponding in vivo value of 396 g of CH4/d. The results of this study showed that the average values proposed by IPCC (2019) can adequately predict CH4 emissions from cows fed typical Mediterranean diets. However, the use of specific factors for the Mediterranean area, such as DE, improved the accuracy of the models.

Enteric Methane Emissions Prediction in Dairy Cattle and Effects of Monensin on Methane Emissions: A Meta-Analysis

Greenhouse gas emissions, such as enteric methane (CH₄) from ruminant livestock, have been linked to global warming. Thus, easily applicable CH₄ management strategies, including the inclusion of dietary additives, should be in place. The objectives of the current study were to: (i) compile a database of animal records that supplemented monensin and investigate the effect of monensin on CH₄ emissions; (ii) identify the principal dietary, animal, and lactation performance input variables that predict enteric CH₄ production (g/d) and yield (g/kg of dry matter intake DMI); (iii) develop empirical models that predict CH₄ production and yield in dairy cattle; and (iv) evaluate the newly developed models and published models in the literature. A significant reduction in CH₄ production and yield of 5.4% and 4.0%, respectively, was found with a monensin supplementation of ≤24 mg/kg DM. However, no robust models were developed from the monensin database because of inadequate observations under the current paper's inclusion/exclusion criteria. Thus, further long-term in vivo studies of monensin supplementation at ≤24 mg/kg DMI in dairy cattle on CH₄ emissions specifically beyond 21 days of feeding are reported to ensure the monensin effects on the enteric CH₄ are needed. In order to explore CH₄ predictions independent of monensin, additional studies were added to the database. Subsequently, dairy cattle CH₄ production prediction models were developed using a database generated from 18 in vivo studies, which included 61 treatment means from the combined data of lactating and non-lactating cows (COM) with a subset of 48 treatment means for lactating cows (LAC database). A leave-one-out cross-validation of the derived models showed that a DMI-only predictor model had a similar root mean square prediction error as a percentage of the mean observed value (RMSPE, %) on the COM and LAC database of 14.7 and 14.1%, respectively, and it was the key predictor of CH₄ production. All databases observed an improvement in prediction abilities in CH₄ production with DMI in the models along with dietary forage proportion inclusion and the quadratic term of dietary forage proportion. For the COM database, the CH₄ yield was best predicted by the dietary forage proportion only, while the LAC database was for dietary forage proportion, milk fat, and protein yields. The best newly developed models showed improved predictions of CH₄ emission compared to other published equations. Our results indicate that the inclusion of dietary composition along with DMI can provide an improved CH₄ production prediction in dairy cattle.

Lentilactobacillus buchneri Preactivation Affects the Mitigation of Methane Emission in Corn Silage Treated with or without Urea

The aim of this study was to investigate the effect of different forms of Lentilactobacillus buchneri on the in vitro methane production, fermentation characteristics, nutritional quality, and aerobic stability of corn silage treated with or without urea. The following treatments were applied prior to ensiling: (1) no urea treatment and LB; (2) no urea treatment+freeze dried LB; (3) no urea treatment+preactivated LB; (4) with urea treatment+no LB; (5) with urea treatment+freeze dried LB; (6) with urea treatment+preactivated. LB was applied at a rate of 3 × 108 cfu/kg on a fresh basis, while urea was applied at a rate of 1% on the basis of dry matter. Data measured at different time points were analyzed according to a completely randomized design, with a 2 × 3 × 5 factorial arrangement of treatments, while the others were analyzed with a 2 × 3 factorial arrangement. Preactivated LB was more effective than freeze-dried LB in reducing silage pH, ammonia nitrogen, cell-wall components, yeast count, and carbon dioxide production, as well as increasing lactic acid and residual water-soluble carbohydrate and aerobic stability (p < 0.0001). A significant reduction in the methane ratio was observed after 24 h and 48 h incubation with preactivated forms of LB (p < 0.001). The results indicated that preactivated LB combined with urea improved fermentation characteristics, nutritional quality, and aerobic stability and reduced the methane ratio of corn silages.

Enteric Methane Emissions and Animal Performance in Dairy and Beef Cattle Production: Strategies, Opportunities, and Impact of Reducing Emissions

Enteric methane (CH4) emissions produced by microbial fermentation in the rumen resulting in the emission of greenhouse gases (GHG) into the atmosphere. The GHG emissions reduction from the livestock industry can be attained by increasing production efficiency and improving feed efficiency, by lowering the emission intensity of production, or by combining the two. In this work, information was compiled from peer-reviewed studies to analyze CH4 emissions calculated per unit of milk production, energy-corrected milk (ECM), average daily gain (ADG), dry matter intake (DMI), and gross energy intake (GEI), and related emissions to rumen fermentation profiles (volatile fatty acids [VFA], hydrogen [H2]) and microflora activities in the rumen of beef and dairy cattle. For dairy cattle, there was a positive correlation (p < 0.001) between CH4 emissions and DMI (R2 = 0.44), milk production (R2 = 0.37; p < 0.001), ECM (R2 = 0.46), GEI (R2 = 0.50), and acetate/propionate (A/P) ratio (R2 = 0.45). For beef cattle, CH4 emissions were positively correlated (p < 0.05−0.001) with DMI (R2 = 0.37) and GEI (R2 = 0.74). Additionally, the ADG (R2 = 0.19; p < 0.01) and A/P ratio (R2 = 0.15; p < 0.05) were significantly associated with CH4 emission in beef steers. This information may lead to cost-effective methods to reduce enteric CH4 production from cattle. We conclude that enteric CH4 emissions per unit of ECM, GEI, and ADG, as well as rumen fermentation profiles, show great potential for estimating enteric CH4 emissions.

Herd-level versus animal-level variation in methane emission prediction in grazing dairy cattle

In response to the increased concern over agriculture's contribution to greenhouse gas (GHG) emissions, more detailed assessments of current methane emissions and their variation, within and across individual dairy farms and cattle, are of interest for research and policy development. This assessment will provide insights into possible changes needed to reduce GHG emissions, the nature and direction of these changes, ways to influence farmer behavior and areas to maximize the adoption of emerging mitigation technologies. The objectives of this study were to (1) quantify the variation in enteric fermentation methane emissions within and among seasonal calving dairy farms with the majority of nutritional requirements met through grazed pasture; (2) use this variation to assess the potential of new individual animal emission monitoring technologies and their impact on mitigation policy. We used a large database of cow performance records for milk production and survival from 2 398 herds in New Zealand, and simulation to account for unobserved variation in feed efficiency and methane emissions per unit of feed. Results showed an average of 120 ± 31.4 kg predicted methane (CH₄) per cow per year after accounting for replacement costs, ranging 8.9-323 kg CH₄/cow per year. Whereas milk production, survival and predicted live weight were reasonably effective at predicting both individual and herd average levels of per cow feed intake, substantial within animal variation in emissions per unit of feed reduced the ability of these variables to predict variation in per animal methane output. Animal-level measurement technologies predicting only feed intake but not emissions per unit of feed are unlikely to be effective for advancing national policy goals of reducing dairy farming enteric methane output. This is because farmers seek to profitably utilize all farm feed resources available, so improvements in feed efficiency will not result in the reduction in feed utilization required to reduce methane emissions. At a herd level, average per cow milk production and live weight could form the basis of assigning a farm-level point of obligation for methane emissions. In conclusion, a comprehensive national database infrastructure that was tightly linked to animal identification and movement systems, and captured live weight data from existing farm-level recording systems, would be required to make this effective. Additional policy and incentivization mechanisms would still be required to encourage farmer uptake of mitigation interventions, such as novel feed supplements or vaccines that reduce methane emissions per unit of feed.

Biogas Plant Exploitation in a Middle-Sized Dairy Farm in Poland: Energetic and Economic Aspects

Although cow manure is a valuable natural fertilizer, it is also a source of extreme greenhouse gas emissions, mainly methane. For this reason, this study aims to determine the impact of investments in a biogas plant on the energy and economic aspects of the operation of a dairy farm. A farm with a breeding size of 600 livestock units (LSU) was adopted for the analysis. In order to reach the paper’s aim, the analysis of two different scenarios of dairy farm functioning (conventional–only milk production, and modern–with biogas plant exploitation) was conducted. The analysis showed that the investment in biogas plant operations at a dairy farm and in using cow manure as one of the main substrates is a more profitable scenario compared to traditional dairy farming. Taking into account the actual Polish subsidies for electricity produced by small biogas plants, the scenario with a functioning biogas plant with a capacity of 500 kW brings €332,000/a more profit compared to the conventional scenario, even when taking into account additional costs, including the purchase of straw to ensure a continuous operation of the installation. Besides, in the traditional scenario, building a biogas plant allows for an almost complete reduction of greenhouse gas emissions during manure storage.