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Summary of quality parameters and validation results of the multiple regression equations predicting methane production (CH 4 , L/d) using the complete data set of 43 milk fatty acid (MFA) variable (data sets 1-3), or a reduced number of MFA variables and groups of MFA (data sets 4-6) 1
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Milk fatty acids (MFA) are a proxy for the prediction of CH4 emission from cows, and prediction differs with diet. Our objectives were (1) to compare the effect of diets on the relation between MFA profile and measured CH4 production, (2) to predict CH4 production based on 6 data sets differing in the number and type of MFA, and (3) to test whether...
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... The savvy interpretation of the MFA / enteric CH 4 emission relationship, coupled with the accessibility and practicality of the MIRS recording technique, provides an incredible opportunity for CH 4 mitigation in a commercial farm setting. This realization has revitalized dairy cattle CH 4 emission prediction, with a wealth of studies investigating the development of a variety of MFA-based, commercially applicable, prediction models (Williams et al., 2014;Bougouin et al., 2019;Engelke et al., 2019). ...
Measuring dairy cattle methane (CH4) emissions using traditional recording technologies is complicated and expensive. Prediction models, which estimate CH4 emissions based on proxy information, provide an accessible alternative. This review covers the different modeling approaches taken in the prediction of dairy cattle CH4 emissions and highlights their individual strengths and limitations. Following the guidelines set out by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA); Scopus, EBSCO, Web of Science, PubMed and PubAg were each queried for papers with titles that contained search terms related to a population of “Bovine,” exposure of “Statistical Analysis or Machine Learning,” and outcome of “Methane Emissions”. The search was executed in December 2022 with no publication date range set. Eligible papers were those that investigated the prediction of CH4 emissions in dairy cattle via statistical or machine learning (ML) methods and were available in English. 299 papers were returned from the initial search, 55 of which, were eligible for inclusion in the discussion. Data from the 55 papers was synthesized by the CH4 emission prediction approach explored, including mechanistic modeling, empirical modeling, and machine learning. Mechanistic models were found to be highly accurate, yet they require difficult-to-obtain input data, which, if imprecise, can produce misleading results. Empirical models remain more versatile by comparison, yet suffer greatly when applied outside of their original developmental range. The prediction of CH4 emissions on commercial dairy farms can utilize any approach, however, the traits they use must be procurable in a commercial farm setting. Milk fatty acids (MFA) appear to be the most popular commercially accessible trait under investigation, however, MFA-based models have produced ambivalent results and should be consolidated before robust accuracies can be achieved. ML models provide a novel methodology for the prediction of dairy cattle CH4 emissions through a diverse range of advanced algorithms, and can facilitate the combination of heterogenous data types via hybridization or stacking techniques. In addition to this, they also offer the ability to improve dataset complexity through imputation strategies. These opportunities allow ML models to address the limitations faced by traditional prediction approaches, as well as enhance prediction on commercial farms.
... With the sole exception of Engelke et al. (2018), who predicted the FA by mid-infrared spectroscopy (MIRs), the milk FA profile was determined by GC. In a later study, these authors also determined the FA profiles by GC (Engelke et al., 2019). In the majority of the studies examined, EME was measured in an RC. ...
Greenhouse gas emission from the activities of all productive sectors is currently a topic of foremost importance. The major contributors in the livestock sector are ruminants, especially dairy cows. This study aimed to evaluate and compare 21 equations for predicting enteric methane emissions (EME) developed on the basis of milk traits and fatty acid profiles, which were selected from 46 retrieved through a literature review. We compiled a reference database of the detailed fatty acid profiles, determined by GC, of 992 lactating cows from 85 herds under 4 different dairy management systems. The cows were classified according to DIM, parity order, and dairy system. This database was the basis on which we estimated EME using the selected equations. The EME traits estimated were methane yield (20.63 ± 2.26 g/kg DMI, 7 equations), methane intensity (16.05 ± 2.76 g/kg of corrected milk, 4 equations), and daily methane production (385.4 ± 68.2 g/d, 10 equations). Methane production was also indirectly calculated by multiplying the daily corrected milk yield by the methane intensity (416.6 ± 134.7 g/d, 4 equations). We also tested for the effects of DIM, parity, and dairy system (as a correction factor) on the estimates. In general, we observed little consistency among the EME estimates obtained from the different equations, with exception of those obtained from meta-analyses of a range of data from different research centers. We found all the EME predictions to be highly affected by the sources of variation included in the statistical model: DIM significantly affected the results of 19 of the 21 equations, and parity order influenced the results of 13. Different patterns were observed for different equations with only some of them in accordance with expectations based on the cow's physiology. Finally, the best predictions of daily methane production were obtained when a measure of milk yield was included in the equation or when the estimate was indirectly calculated from daily milk yield and methane intensity.
... Each period consisted of dietary adaptation for 14 days and 7 days of sampling. Treatments comprised total mixed rations (TMR) based on grass silage containing either a high (65: 35) or low (35:65) forageto-concentrate (FC) ratio supplemented with 0 (HF and LF, respectively) or 5% rapeseed oil (RO) in diet DM (HFO and LFO, respectively; for details, refer to Razzaghi et al. [18]). In addition, the cows received 2 × 300 g of concentrate daily from the milking parlor. ...
... As mentioned earlier, the effect of diets on intake, milk production, energy and N metabolism, and milk fatty acid composition are presented and discussed by Razzaghi et al. [18], but briefly, rapeseed oil supplementation at 5% diet DM tended to decrease DM intake, leading to lower intake of other nutrients (CP, NDF, pdNDF, water-soluble carbohydrate, and starch) without affecting GE intake, which is consistent with the previous studies on oil [34] or oilseeds [35]. On the other hand, cows receiving a diet lower in forage level (65 vs. 35% DM) increased DM intake by about 9%, leading to increased intake of OM, CP, EE, water-soluble carbohydrates, starch, and GE and reduced intake of NDF and pdNDF. ...
An experiment was conducted to examine how dietary interventions reducing enteric methane (CH4) emissions influence manure CH4 emissions in biogas production (as biochemical methane potential (BMP)) or under static conditions mimicking natural manure storage conditions. Experimental treatments consisted of a factorial arrangement of high (HF: 0.65) or low (LF: 0.35) levels of forage and 0 or 50 g of rapeseed oil per kg of diet dry matter. Oil supplementation reduced daily enteric CH4 emissions, especially in the HF diet, by 20%. Greater dietary concentrate proportion reduced CH4 yield and intensity (6 and 12%, respectively) and decreased pH, increased total volatile fatty acids, and molar proportions of butyrate and valerate in feces incubated under static conditions. Oil supplementation increased daily BMP and BMP calculated per unit of organic matter (OM) (17 and 15%, respectively). Increased dietary concentrate had no impact on daily BMP and BMP per unit of OM, whereas it reduced daily CH4 production by 89% and CH4 per unit of OM by 91% under static conditions. Dietary oil supplementation tended to decrease fecal CH4 production per unit of digestible OM (23%) under static conditions. Diets had no impact on the alpha diversity of ruminal prokaryotes. After incubation, the fecal prokaryote community was significantly less diverse. Diets had no effect on alpha diversity in the BMP experiment, but static trial fecal samples originating from the HF diet showed significantly lower diversity compared with the LF diet. Overall, the tested dietary interventions reduced enteric CH4 emissions and reduced or tended to reduce manure CH4 emissions under static conditions, indicating a lack of trade-off between enteric and manure CH4 emissions. The potential for increasing CH4 yields in biogas industries due to dietary interventions could lead to a sustainable synergy between farms and industry.
... Among these, milk mid-infrared spectra, which are readily available from milk recording agencies, and milk volatile fatty acids have been studied extensively. Diet influences milk composition, and these proxies show a wide range of accuracy depending on diet composition (76) or statistical model (77). Predicting methane levels from efficiency-related traits, such as intake or residual feed intake, has also been proposed, but considerable variation has been reported with conflicting results, including a lack of association with methane emissions (63,(78)(79)(80). ...
Mitigation of methane emission, a potent greenhouse gas, is a worldwide priority to limit global warming. A substantial part of anthropogenic methane is emitted by the livestock sector, as methane is a normal product of ruminant digestion. We present the latest developments and challenges ahead of the main efficient mitigation strategies of enteric methane production in ruminants. Numerous mitigation strategies have been developed in the last decades, from dietary manipulation and breeding to targeting of methanogens, the microbes that produce methane. The most recent advances focus on specific inhibition of key enzymes involved in methanogenesis. But these inhibitors, although efficient, are not affordable and not adapted to the extensive farming systems prevalent in low- and middle-income countries. Effective global mitigation of methane emissions from livestock should be based not only on scientific progress but also on the feasibility and accessibility of mitigation strategies.
Expected final online publication date for the Annual Review of Animal Biosciences, Volume 12 is February 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... milk yield, BW), and milk or meat components (Sejian et al. 2011;Ramin and Huhtanen 2013;Patra and Lalhriatpuii 2016). One promising proxy for the prediction of enteric methane production in dairy species is represented by the concentration of milk fatty acids (FAs) Engelke et al. 2019;Requena et al. 2020) because they share biological pathways with enteric methane production (Chilliard et al. 2009;Dijkstra, Van Zijderveld, et al. 2011). Published prediction equations based on milk FA profile are almost all for dairy cattle and they were developed for predicting methane yield per unit of DMI (enteric methane yield (eMY); g CH 4 /kg DMI) and methane intensity per unit of fat/protein-corrected milk (enteric methane intensity (eMI); g CH 4 /kg FPCM). ...
... milk yield, BW), and milk or meat components (Sejian et al. 2011;Ramin and Huhtanen 2013;Patra and Lalhriatpuii 2016). One promising proxy for the prediction of enteric methane production in dairy species is represented by the concentration of milk fatty acids (FAs) Engelke et al. 2019;Requena et al. 2020) because they share biological pathways with enteric methane production (Chilliard et al. 2009;Dijkstra, Van Zijderveld, et al. 2011). Published prediction equations based on milk FA profile are almost all for dairy cattle and they were developed for predicting methane yield per unit of DMI (enteric methane yield (eMY); g CH 4 /kg DMI) and methane intensity per unit of fat/protein-corrected milk (enteric methane intensity (eMI); g CH 4 /kg FPCM). ...
... Nevertheless, many different supplements have been investigated resulting in different reduction potentials (Hristov et al., 2013;Jayanegara et al., 2020). Our assumptions are based on supplementation with linseed, which is relatively well studied and easily available in Switzerland (Engelke et al., 2019;Poteko et al., 2020). Such uncertainties, heterogeneous mitigation potentials and (partially) high costs are among the major challenges of integrating agriculture in general climate policies (Fellmann et al., 2018). ...
To reduce agricultural greenhouse gas (GHG) emissions, farmers need to change current farming practices. However, farmers' climate change mitigation behaviour and particularly the role of social and individual characteristics remains poorly understood. Using an agent‐based modelling approach, we investigate how knowledge exchange within farmers' social networks affects the adoption of mitigation measures and the effectiveness of a payment per ton of GHG emissions abated. Our simulations are based on census, survey and interview data for 49 Swiss dairy and cattle farms to simulate the effect of social networks on overall GHG reduction and marginal abatement costs. We find that considering social networks increases overall reduction of GHG emissions by 45% at a given payment of 120 Swiss Francs (CHF) per ton of reduced GHG emissions. The per ton payment would have to increase by 380 CHF (i.e., 500 CHF/tCO 2 eq) to reach the same overall GHG reduction level without any social network effects. Moreover, marginal abatement costs for emissions are lower when farmers exchange relevant knowledge through social networks. The effectiveness of policy incentives aiming at agricultural climate change mitigation can hence be improved by simultaneously supporting knowledge exchange and opportunities of social learning in farming communities.
... Furthermore, numerous studies have demonstrated that methane production is closely related to the weight of the animal, the amount of dry matter ingested, and the amount of gross energy ingested [11,12]. It is recognized that methane production is a hereditable trait, and that genetic selection for low-emitting ruminants is an effective mitigation option, assuming feed intake and animal productivity remain unchanged [13,14]. ...
A significant portion of global greenhouse gas emissions is attributed to methane (CH4), the primary greenhouse gas released by dairy animals. Thus, livestock farming has a new challenge in reducing enteric CH4 for sustainability. In anaerobic microbial ecosystems such as the rumen, carbohydrates are converted into short-chain, volatile fatty acids that animals use for energy and protein synthesis. It is, therefore, essential to understand rumen physiology, population dynamics, and diversity to target methanogens. Thus far, numerous CH4 mitigation strategies have been studied , including feeding management, nutrition, rumen modification, genetics, and other approaches for increasing animal production. As new molecular techniques are developed, scientists have more opportunities to select animals with higher genetic merit through next-generation sequencing. The amount of CH4 produced per unit of milk or meat can be permanently and cumulatively reduced through genetic selection. Developing eco-friendly and practical nutrigenomic approaches to mitigating CH4 and increasing ruminant productivity is possible using next-generation sequencing techniques. Therefore, this review summarizes current genetic and nutrigenomic approaches to reducing enteric CH4 production without posing any danger to animals or the environment.
... However, the use of ECM reduced the predictive ability of the model. Some recent studies have shown that milk fatty acids are generated by enteric fiber fermentation, and that these acids could be used alone or in combination with milk yield to predict enteric CH 4 emissions (Engelke et al. 2019). In addition, previous studies have suggested that enteric CH 4 emissions might not follow a linear pattern, and thus nonlinear models might better describe enteric function and fermentation dynamics compared with linear models (Huhtanen et al. 2019). ...
Methane (CH4) emissions from ruminant production are a significant source of anthropogenic greenhouse gas production, but few studies have examined the enteric CH4 emissions of lactating dairy cows under different feeding regimes in China. This study aimed to investigate the influence of different dietary neutral detergent fiber/non-fibrous carbohydrate (NDF/NFC) ratios on production performance, nutrient digestibility, and CH4 emissions for Holstein dairy cows at various stages of lactation. It evaluated the performance of CH4 prediction equations developed using local dietary and milk production variables compared to previously published prediction equations developed in other production regimes. For this purpose, 36 lactating cows were assigned to one of three treatments with differing dietary NDF/NFC ratios: low (NDF/NFC = 1.19), medium (NDF/NFC=1.54), and high (NDF/NFC=1.68). A modified acid-insoluble ash method was used to determine nutrient digestibility, while the sulfur hexafluoride technique was used to measure enteric CH4 emissions. The results showed that the dry matter (DM) intake of cows at the early, middle, and late stages of lactation decreased significantly (P
... Methane emissions from dairy cows are of concern by the public in some countries. Several authors (Rico et al. 2015;Bougouin et al. 2019;Engelke et al. 2019) have described the usage of milk FA profiles of individual cow milk samples for prediction of methane emission. More specifically, the full milk FA profile was determined using GC and certain FA (e.g. ...
Milk fat is an important nutrient for young animals and humans, and its composition [fatty acids (FA)] may vary broadly. The objective of this study was to describe the development of global FA models for high‐throughput Fourier transform infrared milk analysers. The developed models for major FA, FA grouped by chain length, by degree of unsaturation and by their origin, showed good accuracy, repeatability and correlation to gas chromatography. Moderate performance was obtained for low concentration FA (short chain, polyunsaturated and trans). Possible practical applications could be to enhance dairy products, optimise milk processing and/or optimise dairy cow performance through improved feeding and management. Milk sample from individual cow or bulk tank. FTIR‐based high throughput milk analyser to determine milk fatty acid profile. Production of dairy products with enhanced nutritional value. Optimisation of dairy cow feeding.
... Also, the presence of odd-and branched-chain fatty acids (OBCFA) in milk is due to microbial synthesis and can be associated with rumen functions, including VFA and methane production (Vlaeminck et al., 2006;Fievez et al., 2012). Based on this, numerous studies were performed to identify discriminant FA and link their concentration to enteric methane production (e.g., Chilliard et al., 2009;Dijkstra et al., 2011;Mohammed et al., 2011;Rico et al., 2016;Engelke et al., 2019). The identification and quantification of milk FA is done using GC coupled to a flame ionization detector (GC-FID), although the ability of mid-infrared spectroscopy to predict the quantity of certain milk FA (and other compounds in the mid-infrared spectrum) in a rapid and economic way (De Marchi et al., 2014) has also prompted researchers to test its application as a predictor of methane emissions in dairy cows (e.g., Dehareng et al., 2012;Vanlierde et al., 2015;Shetty et al., 2017;van Gastelen et al., 2018b). ...
... Notwithstanding, the general consensus is that milk FA have, at best, a moderate predictive power for methane emissions (Mohammed et al., 2011;van Gastelen et al., 2018b;Bougouin et al., 2019a). The prediction power of models can be improved when other parameters are included such as intake and when they are applied to the diets on which they were validated (Mohammed et al., 2011;Rico et al., 2016;Engelke et al., 2019). A probable reason for this lack of universality of prediction models is that milk FA are highly influenced by diet (Newbold and Ramos-Morales, 2020). ...
Metabolome profiling in biological fluids is an interesting approach for exploring markers of methane emissions in ruminants. In this study, a multiplatform metabolomics approach was used for investigating changes in milk metabolic profiles related to methanogenesis in dairy cows. For this purpose, 25 primiparous Holstein cows at similar lactation stage were fed the same diet supplemented with (treated, n = 12) or without (control, n = 13) a specific antimethanogenic additive that reduced enteric methane production by 23% with no changes in intake, milk production, and health status. The study lasted 6 wk, with sampling and measures performed in wk 5 and 6. Milk samples were analyzed using 4 complementary analytical methods, including 2 untargeted (nuclear magnetic resonance and liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer) and 2 targeted (liquid chromatography-tandem mass spectrometry and gas chromatography coupled to a flame ionization detector) approaches. After filtration, variable selection and normalization data from each analytical platform were then analyzed using multivariate orthogonal partial least square discriminant analysis. All 4 analytical methods were able to differentiate cows from treated and control groups. Overall, 38 discriminant metabolites were identified, which affected 10 metabolic pathways including methane metabolism. Some of these metabolites such as dimethylsulfoxide, dimethylsulfone, and citramalic acid, detected by nuclear magnetic resonance or liquid chromatography-mass spectrometry methods, originated from the rumen microbiota or had a microbial-host animal co-metabolism that could be associated with methanogenesis. Also, discriminant milk fatty acids detected by targeted gas chromatography were mostly of ruminal microbial origin. Other metabolites and metabolic pathways significantly affected were associated with AA metabolism. These findings provide new insight on the potential role of milk metabolites as indicators of enteric methane modifications in dairy cows.
