Prediction of enteric methane production from dairy cows

Global Warming and Dairy Cattle: How to Control and Reduce Methane Emission

Agriculture produces greenhouse gases. Methane is a result of manure degradation and microbial fermentation in the rumen. Reduced CH4 emissions will slow climate change and reduce greenhouse gas concentrations. This review compiled studies to evaluate the best ways to decrease methane emissions. Longer rumination times reduce methane emissions and milk methane. Other studies have not found this. Increasing propionate and reducing acetate and butyrate in the rumen can reduce hydrogen equivalents that would otherwise be transferred to methanogenesis. Diet can reduce methane emissions. Grain lowers rumen pH, increases propionate production, and decreases CH4 yield. Methane generation per unit of energy-corrected milk yield reduces with a higher-energy diet. Bioactive bromoform discovered in the red seaweed Asparagopsis taxiformis reduces livestock intestinal methane output by inhibiting its production. Essential oils, tannins, saponins, and flavonoids are anti-methanogenic. While it is true that plant extracts can assist in reducing methane emissions, it is crucial to remember to source and produce plants in a sustainable manner. Minimal lipid supplementation can reduce methane output by 20%, increasing energy density and animal productivity. Selecting low- CH4 cows may lower GHG emissions. These findings can lead to additional research to completely understand the impacts of methanogenesis suppression on rumen fermentation and post-absorptive metabolism, which could improve animal productivity and efficiency.

A Basic Model to Predict Enteric Methane Emission from Dairy Cows and Its Application to Update Operational Models for the National Inventory in Norway

The aim of this study was to develop a basic model to predict enteric methane emission from dairy cows and to update operational calculations for the national inventory in Norway. Development of basic models utilized information that is available only from feeding experiments. Basic models were developed using a database with 63 treatment means from 19 studies and were evaluated against an external database (n = 36, from 10 studies) along with other extant models. In total, the basic model database included 99 treatment means from 29 studies with records for enteric CH₄ production (MJ/day), dry matter intake (DMI) and dietary nutrient composition. When evaluated by low root mean square prediction errors and high concordance correlation coefficients, the developed basic models that included DMI, dietary concentrations of fatty acids and neutral detergent fiber performed slightly better in predicting CH₄ emissions than extant models. In order to propose country-specific values for the CH₄ conversion factor Y_m (% of gross energy intake partitioned into CH₄) and thus to be able to carry out the national inventory for Norway, the existing operational model was updated for the prediction of Y_m over a wide range of feeding situations. A simulated operational database containing CH₄ production (predicted by the basic model), feed intake and composition, Y_m and gross energy intake (GEI), in addition to the predictor variables energy corrected milk yield and dietary concentrate share were used to develop an operational model. Input values of Y_m were updated based on the results from the basic models. The predicted Y_m ranged from 6.22 to 6.72%. In conclusion, the prediction accuracy of CH₄ production from dairy cows was improved with the help of newly published data, which enabled an update of the operational model for calculating the national inventory of CH₄ in Norway.

In Vitro Assessment of Enteric Methane Emission Potential of Whole-Plant Barley, Oat, Triticale and Wheat

The study determined in vitro enteric methane (CH₄) emission potential of whole-plant cereal (WPC) forages in relationship to nutrient composition, degradability, and rumen fermentation. Two varieties of each WPC (barley, oat, triticale, and wheat) were harvested from two field replications in each of two locations in central Alberta, Canada, and an in vitro batch culture technique was used to characterize gas production (GP), fermentation, and degradability. Starch concentration (g/kg dry matter (DM)) was least (p < 0.001) for oat (147), greatest for wheat (274) and barley (229), and intermediate for triticale (194). The aNDF concentration was greater for oat versus the other cereals (531 vs. 421 g/kg DM, p < 0.01). The 48 h DM and aNDF degradabilities (DMD and aNDFD) differed (p < 0.001) among the WPCs. The DMD was greatest for barley, intermediate for wheat and triticale, and least for oat (719, 677, 663, and 566 g/kg DM, respectively). Cumulative CH₄ production (MP; mL) from 12 h to 48 h of incubation was less (p < 0.001) for oat than the other cereals, reflecting its lower DMD. However, CH₄ yield (MY; mg of CH₄/g DM degraded) of barley and oat grown at one location was less than that of wheat and triticale (28 vs. 31 mg CH₄/g DM degraded). Chemical composition failed to explain variation in MY (p = 0.35), but it explained 45% of the variation in MP (p = 0.02). Variation in the CH₄ emission potential of WPC was attributed to differences in DMD, aNDFD, and fermentation end-products (R² ≥ 0.88; p < 001). The results indicate that feeding whole-plant oat forage to ruminants may decrease CH₄ emissions, but animal performance may also be negatively affected due to lower degradability, whereas barley forage may ameliorate emissions without negative effects on animal performance.

Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand

There are several models in the literature for predicting enteric methane (CH4 ) emissions. These models were often developed on region or country-specific data and may not be able to predict the emissions successfully in every region. The majority of extant models require dry matter intake (DMI) of individual animals, which is not routinely measured. The objectives of this study were to (i) evaluate performance of extant models in predicting enteric CH4 emissions from dairy cows in North America (NA), Europe (EU), and Australia and New Zealand (AUNZ) and (ii) explore the performance using estimated DMI. Forty extant models were challenged on 55, 105, and 52 enteric CH4 measurements (g per lactating cow per day) from NA, EU, and AUNZ, respectively. The models were ranked using root mean square prediction error as a percentage of the average observed value (RMSPE) and concordance correlation coefficient (CCC). A modified model of Nielsen et al. (Acta Agriculturae Scand Section A, 63, 2013 and 126) using DMI, and dietary digestible neutral detergent fiber and fatty acid contents as predictor variables, were ranked highest in NA (RMSPE = 13.1% and CCC = 0.78). The gross energy intake-based model of Yan et al. (Livestock Production Science, 64, 2000 and 253) and the updated IPCC Tier 2 model were ranked highest in EU (RMSPE = 11.0% and CCC = 0.66) and AUNZ (RMSPE = 15.6% and CCC = 0.75), respectively. DMI of cows in NA and EU was estimated satisfactorily with body weight and fat-corrected milk yield data (RMSPE < 12.0% and CCC > 0.60). Using estimated DMI, the Nielsen et al. (2013) (RMSPE = 12.7 and CCC = 0.79) and Yan et al. (2000) (RMSPE = 13.7 and CCC = 0.50) models still predicted emissions in respective regions well. Enteric CH4 emissions from dairy cows can be predicted successfully (i.e., RMSPE < 15%), if DMI can be estimated with reasonable accuracy (i.e., RMSPE < 10%).