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

Niu, Puchun; Schwarm, Angela; Bonesmo, Helge; Kidane, Alemayehu; Åby, Bente Aspeholen; Storlien, T. M.; Kreuzer, Michael; Álvarez, Clementina; Sommerseth, Jon Kristian; Prestløkken, Egil

doi:10.3390/ani11071891

Cited by 8 publications

(8 citation statements)

References 53 publications

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“…There are many models from various studies. Storlien et al and Niu et al created a database for the basic models that show us CH 4 production, DMI, and contents of EE or FAs and NDF in diets for dairy cows and roughage composition [ 62 , 63 ]. A shortened version of the authors’ database is presented in Table 2 .…”

Section: Measurement and Estimation Of Chmentioning

confidence: 99%

“…Ideally, these models can imitate system dynamics at lower levels of aggregation and can forecast a wider range of eventualities. Because of the multiple inputs and computing needs, dynamic models are difficult to apply to realistic predictions [ 61 , 63 , 64 ].…”

Section: Measurement and Estimation Of Chmentioning

confidence: 99%

“…DMI and CH 4 production had a substantial positive connection, suggesting that as a dairy cow eats more feed, more CH 4 is generated due to increased substrate availability for microbial fermentation [ 63 ] ( Figure 2 ). The findings of the Niu et al study analyzed the influence of explanatory factors on the variability of CH 4 production among areas.…”

Section: Measurement and Estimation Of Chmentioning

confidence: 99%

“…When all other variables were held constant, the slopes of DMI to CH4 production varied from 13.0 to 15.3 g of CH 4 /kg of DMI for the EU cows. The equivalent values for US cows were lower, ranging from 11.3 to 12.3 g of CH 4 /kg of DMI [ 63 ]. Another study found a 2.1% decrease in Ym per kg DMI rise from dairy cows [ 65 ].…”

Section: Measurement and Estimation Of Chmentioning

confidence: 99%

See 3 more Smart Citations

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

Bačėninaitė

Džermeikaitė

Antanaitis

2022

Animals

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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.

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Section: Measurement and Estimation Of Chmentioning

confidence: 99%

Section: Measurement and Estimation Of Chmentioning

confidence: 99%

Section: Measurement and Estimation Of Chmentioning

confidence: 99%

Section: Measurement and Estimation Of Chmentioning

confidence: 99%

See 2 more Smart Citations

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

Bačėninaitė

Džermeikaitė

Antanaitis

2022

Animals

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“…Nevertheless, there was seemingly no effect on rumen protozoal populations, which suggests that the corn oil dosage used in the Zhang study did not act as an anti-protozoal agent 90 . In addition, high levels of PUFAs in the diet will also likely impact the metabolism of various bacterial and archaeal species [88][89][90] , particularly keystone fibre degrading Grampositive bacterial species that produce various levels of VFAs and hydrogen 92,93 . Collectively, these previous findings were in agreement with the decreased CH4 production in cattle and goats in this study fed the COS diet, which was observed to additionally impact other bacterial metabolism and ruminal fermentation parameters, such as increased propionate and decreased butyrate and acetate levels (Table 1) 5 .…”

Section: Protozoa Are Less Active In Animals That Produce Lower Metha...mentioning

confidence: 99%

Metabolic influence of core ciliates within the rumen microbiome

Andersen

Altshuler

Leon

et al. 2022

Preprint

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Protozoa comprise a major fraction of the microbial biomass in the rumen microbiome, of which the genus Entodinium has been consistently observed to be dominant across a diverse genetic and geographical range of ruminant hosts. Despite the apparent core role that species such as Entodinium caudatum exert, their major biological and metabolic contributions to rumen function remain largely undescribed. Here, we have leveraged (meta)genome-centric metaproteomes from rumen fluid samples originating from both cattle and goats fed diets with varying inclusion levels of lipids and starch, to detail the specific metabolic niches that E. caudatum occupies in the context of its microbial co-habitants. Initial proteome estimations via total protein counts and label-free quantification highlight that E. caudatum comprises an extensive fraction of the total rumen metaproteome. Our analysis also suggested increased microbial predation and volatile fatty acid (VFA) metabolism by E. caudatum to occur in high methane-emitting animals, although with no apparent direct metabolic link to methanogenesis. Despite E. caudatum having a well-established reputation for digesting starch, it was unexpectedly less detectable in low methane emitting-animals fed high starch diets, which were instead dominated by propionate/succinate-producing bacterial populations suspected of being resistant to predation irrespective of host. Finally, we reaffirmed our abovementioned observations in geographically independent datasets, thus illuminating the substantial metabolic influence that under-explored eukaryotic populations have in the rumen, with greater implications for both digestion and methane metabolism.

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Forecasting Methane Data Using Multivariate Long Short-Term Memory Neural Networks

Luo,

Wang,

Gates

2024

Environ Model Assess

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Over the past few decades, Alberta has witnessed a remarkable expansion in its oil and gas sector. Unfortunately, this growth has come at a cost, as Alberta has become the fastest-growing source of pollutant emissions in greenhouse gases (GHGs), sulphur emissions, and water pollution in Canada. Among these GHGs, methane stands out as the second most prevalent GHG, possessing a global warming potential ~ 28 times higher than carbon dioxide over a span of 100 years, and ~ 80 times higher over a period of 20 years. Since 1986, the Alberta Energy Regulator (AER) has been diligently gathering data on methane concentrations. Although this data is publicly available, its analysis has not been thoroughly explored. Our study aims to investigate the impact of temperature, wind speed, and wind direction on the predictions of methane concentration time series data, utilizing a long short-term memory (LSTM) neural network model. Our findings indicate that the inclusion of climate variables enhances the predictive capabilities of the LSTM model. However, the results show that it is not obvious which variable has the most impact on the improvement although temperature appears to have a better effect on improving predictive performance compared to wind speed and direction. The results also suggest that the variance of the input data does not affect forecasting performance.

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

Cited by 8 publications

References 53 publications

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

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

Metabolic influence of core ciliates within the rumen microbiome

Forecasting Methane Data Using Multivariate Long Short-Term Memory Neural Networks

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