Seasonal Effect of Grass Nutritional Value on Enteric Methane Emission in Islands Pasture Systems

Quantifying entericCH4 from grazing systems is a challenge for all regions of the world, especially when cattle feed mostly on pasture throughout the year, as pasture quality varies with the seasons. In this study, we examine the influence of seasonality on enteric methane emissions in the Azores, considering the most recent IPCC updates, to minimise errors in estimating enteric methane emissions in this region. For this purpose, samples of corn and grass silage, different types of concentrate, and pasture were collected throughout the year, and their nutritional value and digestibility were determined according to standard conventional methods. The estimation of methane production was conducted using the 2006 IPCC Tier 2 methodology, refined in 2019. The results revealed significant differences (p < 0.05) between the chemical composition of winter and summer pastures. However, it was in the autumn that these pastures presented the best nutritional quality. We estimated that the total volume of enteric methane produced in the Azores was 20,341 t CH4, with peak enteric methane emissions (5837 t CH4) reached during the summer. Breeding bulls, beef cows, and heifers are the categories that produce the highest amount of methane per animal. However, if we consider the total number of animals existing in the region, pregnant dairy cows are the category of cattle with the highest emissions of CH4. Thus, considering the current system of cattle production in the region, we can infer that the pastures are better managed during the autumn, which translates into lower emissions of enteric methane into the atmosphere during this season.

Enteric Methane Emissions in Dairy Cows with Different Genetic Groups in the Humid Tropics of Costa Rica

Enteric methane (CH₄) is one of the main greenhouse gases emitted in livestock production systems with ruminants. Among the options to reduce such emissions, animal genetics is one of the factors that is taking relevance in recent years. The aim of the present study was to assess the emission of enteric CH₄ in dairy cows with different genetic backgrounds. Sixteen cows belonging to the following three genetic groups were selected for this study: seven F1 (50% Jersey × 50% Gyr), five Triple cross (50% Jersey × 31% Holstein × 19% Sahiwal) and four Jersey. Enteric CH₄ emissions were measured in all cows for 15 months, at the middle of each month, using the SF₆ technique. Enteric CH₄ emissions did not differ (p > 0.05) among genetic groups, although it varied with the stage of lactation, due to differences in milk yield and dry matter intake (DMI). Pasture DMI and the intensity of CH₄ emissions (g kg^-1 DMI) differed (p < 0.05) between dry and lactating cows, with higher DMI in the lactation period, while CH₄ emission intensity was higher for dry cows. Cows with the highest proportion of Bos taurus genes presented a higher annual mean methane conversion factor (Y_m), with 7.22, 7.05 and 5.90% for the Triple cross, purebred Jersey and F1, respectively. In conclusion, non-significant differences in enteric CH₄ emissions and Y_m were detected among dairy cows with different genetic backgrounds. However, F1 cows tended to show lower enteric CH₄ emission and Y_m, compared to those with more Bos taurus genes.

Prediction of enteric methane emissions from lactating cows using methane to carbon dioxide ratio in the breath

The aim of this study was to develop prediction equations for methane (CH₄) emissions from lactating cows using the CH₄/carbon dioxide (CO₂) ratio in the breath measured in the automatic milking system (AMS) and to evaluate the predicted values and factors affecting the CH₄/CO₂ ratio. The model development was conducted using a dataset determined in respiration chambers or head boxes (n = 121). Then, gas measurements in the AMS as well as in the head box were carried out with six lactating cows fed one of three different levels of neutral detergent fiber (NDF) content, following a 3 × 3 Latin square experimental design. The obtained equation that is suitable for practical use on farms to predict CH₄ was CH₄ (L/day) = −507 + 0.536 live weight (kg) + 8.76 energy‐corrected milk (kg/day) + 5,029 CH₄/CO₂ (adjusted R² = 0.83; root mean square error = 40.8 L/day). Results showed that the predicted values correlated positively with the observed values, the determined CH₄/CO₂ ratio increased with increasing dietary NDF content, and the detected eructation rate was in the normal range. On the other hand, the CH₄/CO₂ ratio was affected by the time interval between measurement and last eating before the measurement.

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.

Modelling methane emission mitigation by anaerobic digestion: effect of storage conditions and co-digestion

In this work the methane conversion factor (MCF) of untreated and anaerobically digested cattle manure (CM) as a function of storage temperature, time and co-digestion was measured in an in vitro experiment and modelled based on IPCC (2006) methodology (Tier 2). For this, one sample of untreated CM, one sample of mono-digested CM and three samples of CM co-digested with grass were incubated at seven different temperatures (from 5°C to 50°C) over 346 days. The main results showed that ultimate methane yield (B₀) of CM is higher than the B₀ reported by the IPCC (2006). Two temperature ranges should be considered for MCF evolution, below 15°C very low MCF was measured in this work for untreated CM, mono and co-digested samples. At higher temperatures, MCF obtained in this work and that provided by the IPCC could be comparable depending on storage time. Anaerobic mono-digestion decreased MCF compared to untreated CM at all temperatures and times, except in the temperature range between 20°C and 25°C if storage time is low, due to a lag phase observed in CM. This lag phase would probably not happen in real storage conditions depending on the proportion of old manure remaining in the storage tank. Co-digestion with grass-decreased MCF compared to mono-digestion, but increased CH₄ production in terms of fresh matter due to the higher B₀ of the mixture. Storage time, temperature and co-digestion should be considered in the quantification of CH₄ emission from digested material.