This fortnightly newsletter was curated and edited by: J.W. Holloway and his Team
Synopsis
The livestock sector represents a significant source of greenhouse gas (GHG) emissions worldwide, generating carbon dioxide (CO2), methane (CH4; i.e., enteric CH4 in this review), and nitrous oxide (N2O) either directly (e.g., from enteric fermentation and manure management) or indirectly (e.g., from feed-production activities and conversion of forest into pasture). Using a life cycle assessment (LCA) approach and accounting for land-use change, Steinfeld et al. (2006) estimated that the livestock sector contributes about (18%) eighteen percent of the total global anthropogenic GHG emissions. Based on data by the U.S. Environmental Protection Agency (USEPA, 2006), the direct livestock contribution to non-CO2 emissions (i.e., CH4 and N2O) can be estimated at (7.3) and (7.5%) of the global GHG emission values for 2010 and 2020.
Though it can be even lower for some industrialized countries (3.1%), three-point one percent of the total U.S. GHG emissions in 2009; USEPA, 2011). Enteric fermentation and manure decomposition, the processes responsible for CH4 and N2O emissions, are the main targets of GHG mitigation practices for the livestock industries. Discussions in this review are based on a recent comprehensive review of non-CO2 GHG mitigation measures for the livestock sector by Hristov et al. (2013b). The second (Montes et al., 2013) and third (Hristov et al., 2013c) papers in this series address CH4 and N2O emissions from manure decomposition and animal management-related CH4 and N2O mitigation strategies, respectively. Interactions among mitigation practices for individual components of livestock production systems are discussed in Gerber et al. (2013).
Commentary
This limited series of the occasional e-letters are comprised of (5) five articles. They will appear fortnightly and are published during September, October, and November though they will be accessible through our social media pages.
Analysis
FEEDS AND FEEDING MANAGEMENT
There is a clear relationship between feed OM digestibility, concentrate feed or starch intake, and the pattern of ruminal fermentation. As argued by Wolin (1960), the stoichiometry of ruminal fermentation dictates that more hydrogen, and consequently CH4, will be produced with the fermentation of fiber as compared with starch (in the latter case reducing equivalents are used for propionate synthesis). In a meta-analysis, Bannink et al. (2008) predicted that the fermentation of sugars and starch shifted rumen fermentation towards the production of propionate when pH in the rumen decreased. Indeed, a (72) seventy-two vs. ( 52%) fifty-two percent concentrate diet produced a (59%) fifty-nine percent increase in rumen propionate concentration and a (44%) forty-four percent drop in Ac: Pr ratio in lactating dairy cows, accompanied by milk fat depression ((3.20) three-point two vs. (4.20%) four point two percent, respectively; Agle et al., 2010). Sauvant et al. (2011) proposed a quadratic relationship between Ym and Ac:Pr in ruminal fluid: Ym = –1.89 + (4.61 × Ac:Pr) – (0.59 × Ac:Pr2) (n = 23 experiments). Therefore, because of the strong relationship between forage: concentrate and Ac: Pr, increasing inclusion of grain (or feeding forages with higher starch content, such as whole-crop cereal silages) in ruminant diets should lower CH4 production.
Effect of Feed Intake and Inclusion of Concentrates
Feed intake is an essential variable in predicting CH4 emission. Johnson and Johnson (1995) stated that as feed intake increases, the Ym factor decreases by about (1.6%) one point six percentage points per each level of intake above maintenance. Similarly, a linear decrease in Ym with increasing feed intake was reported by Sauvant and Giger-Reverdin (2009). Increasing feed intake, however, usually increases fractional passage rate and decreases digestibility (NRC, 2001). Though may increase excretion of fermentable OM with manure and thus CH4 or N2O emissions, depending on the type of manure handling system. A strong relationship of DMI with ruminal CH4 production has been reported by Cottle et al. (2011), Kennedy and Charmley (2012), and others and was also derived from the dataset of studies used in the report by Hristov et al. (2013b): CH4 (g/d) = 2.54 (SE = 4.89) + 19.14 (SE = 0.43) × DMI (kg/d) (R2 = 0.86, P < 0.001).
This simple relationship, however, ignores diet nutrient composition, which can have a significant impact on ruminal fermentation and CH4 production. Meta-analyses by Yan et al. (2000), Sauvant and Giger-Reverdin (2009), and more recently by Ramin and Huhtanen (2013) have proposed CH4 prediction equations involving intake or concentration of dietary variables such as OM, NDF, ADF, non-fiber carbohydrates, EE, and level of concentrate inclusion. Ellis et al. (2010) evaluated 9 CH4 prediction equations that are currently being used in whole farm GHG models. In their analysis, equations that attempt to represent important aspects of diet composition performed better than more generalized equations. To investigate the relationships among dietary nutrients and CH4 production, the authors of this document developed prediction equations and identified key animal and dietary characteristics that determine CH4 production in cattle.
Data consisted of indirect calorimetric records of lactating and nonlactating cows. Diet characteristics (fiber fractions, CP, EE, and lignin), animal information (BW and breed), GEI, and year of the study were used as possible covariates that could be selected with equal probability (for details on variables selection and statistical procedures see Moraes et al., 2013). The following equations were developed for lactating and nonlactating dairy animals [in which CH4 is expressed on a GEI basis (Mcal/d). NDF is expressed as percent NDF in the diet (DM basis), EE is expressed as percent ether extract in the diet (DM basis), and BW is expressed in kilograms].
Lactating cows: CH4 = 0.37 (0.37) + 0.0392 (0.0015) GEI + 0.0189 (0.0077) NDF – 0.156 (0.034) EE + 0.0014 (0.0003) BW
Nonlactating animals: CH4 = 0.074 (0.093) + 0.0409 (0.0019) GEI + 0.0039 (0.0016) NDF – 0.0432 (0.0122) EE + 0.0014 (0.00008) BW
Although equations such as the above can be useful for predicting changes in CH4 production triggered by changes in diet ingredient or nutrient composition, they have limitations in predicting the effects of mitigation strategies. Mechanistic models that describe the mechanism of CH4 production based on knowledge of degradation processes in the rumen and type of VFA formed give better predictions than empirical models (e.g., Alemu et al., 2011) and might provide insights into possible mitigation options. Indeed, a mechanistic model is now used for GHG inventory purposes in the Netherlands as an IPCC Tier (3) three alternatives to the IPCC Tier (2) two fixed Ym approach to estimate CH4 production by dairy cattle. Unlike the Tier (2) two approach, the Tier (3) three approach does show different behavior in CH4 production in the past two decades when compared with the Tier (2) two method because the mechanistic model is capable of representing changes in CH4 production that result from changes in diet composition that occurred over these (2)two decades (Bannink et al., 2011).
Importantly, dietary variables are not independent, and increasing or decreasing the concentration of one entity will decrease or increase concentration of another. For example, as discussed earlier, mitigation options aimed at reducing urinary N excretion may result in elevated CH4 emission (Dijkstra et al., 2011). Decreasing dietary concentration of CP will result in increasing concentration of other nutrients (such as starch or NDF), and these changes may affect enteric and manure CH4 and N2O emissions. Therefore, effects on GHG emissions as a result of changes in one nutrient must be interpreted in the context of potential impacts resulting from changes in other dietary constituents. Increasing the proportion of concentrate in the diet will lower CH4 emissions per unit of feed intake and animal product if production remains the same or is increased, which has been demonstrated in the classic works of Flatt et al. (1969) and Tyrrell and Moe (1972) and reinforced by others (Ferris et al., 1999; Yan et al., 2000).
Some experiments with lactating dairy cows and beef cattle have shown linear decreases in CH4 emissions with an increase in the proportion of concentrate in the diet (Aguerre et al., 2011; McGeough et al., 2010). In a meta-analysis of (87), eighty-seven experiments with (260) two hundred sixty treatments involving growing and lactating cattle, sheep, and goats, Sauvant and Giger-Reverdin (2009) concluded that marked improvements in Ym can be expected beyond (35) thirty-five to (40%) forty percent inclusion of grain in the diet and this was also dependent on the level of feed intake (Fig. (2) two). Based on these data, small and moderate variation in dietary concentrate proportion is unlikely to affect CH4 emission. However, concentrates generally provide more digestible nutrients (per unit feed) than roughage, which could increase animal productivity. For example, Huhtanen and Hetta (2012), in a meta-analysis of (986) nine hundred eighty-six dietary treatments, reported a highly significant and positive relationship between dietary concentrate intake and production of milk, energy-corrected milk, and milk fat and milk protein. Hence, CH4 expressed per unit product (i.e., Ei) is likely to decrease.
Increasing the concentrate proportion in the diet above certain levels, however, might have a negative effect on fiber digestibility (Firkins, 1997; Nousiainen et al., 2009; Agle et al., 2010; Ferraretto et al., 2013), which, in addition to a potential loss of production, could result in increased concentration of fermentable OM in manure and perhaps increased CH4 emissions from stored manure (Lee et al., 2012). Grain processing itself can have a significant effect on starch concentration in feces. Total tract digestibility of steam-flaked corn, for example, was (25%) twenty-five percent higher than that of steam-rolled corn grain in dairy cows (Firkins et al., 2001). Inclusion of steam-rolled corn (vs. steam flaked) in beef cattle fed finishing diets ((80%) eighty percent concentrate) resulted in extremely high starch concentrations in feces (Depenbusch et al., 2008). Therefore, a deductive notation is decreased CH4 production (per unit of DMI) due to increased inclusion of grain in the diet may be partially offset by increased CH4 emission from manure. To what extent these (2) two processes will take place in an area that needs to be investigated and included in prediction models.
Mixed Rations and Feeding Frequency
Very little research is available on the effect of feeding system (i.e., component or choice feeding of forage and concentrates vs. feeding of TMR) on CH4 production. The advantages of feeding complete rations (i.e., TMR) are a more precise nutrient allocation (Coppock, 1977) and a more precise feeding of micronutrient supplements. Nocek et al. (1986) fed dairy cows forage and concentrate separately or as TMR and observed higher FCM feed efficiency with the separate feeding system due to lower feed intake. In contrast, Maekawa et al. (2002) did not report any differences in feed intake or milk production and composition of dairy cows fed ingredients as a TMR or separately. They concluded that the latter increased the risk of acidosis because cows ate a more significant proportion of concentrate than intended (overall rumen pH tended to be lower when compared with the (50%) fifty percent forage: (50%) fifty percent concentrate TMR). More research is needed to determine feeding regimes that improve feed efficiency and lower CH4 Ei. Very few studies have investigated the effect of feeding frequency on CH4 emissions.
The reason for including this discussion in relation to CH4 emission is that synchronization of energy and protein availability in the rumen has long been proposed as a tool for optimizing rumen function and maximizing microbial protein synthesis. Earlier studies investigated the effect of feeding frequency from the perspective of optimizing carbohydrate fermentation in the rumen. Mathers and Walters (1982), for example, fed sheep every two hands concluded that, even with frequent feeding, there was a considerable deviation from steady-state in the rate of carbohydrate fermentation in the rumen. Methane production increased rapidly, within (30 min), thirty minutes after feeding and then decreased until the next 2-h cycle. A series of trials in the 1980s from the laboratory of M. Kirchgessner at the University of Munchen in Germany found that frequent feeding did not improve dietary energy use but did increase CH4 emission when the concentrate was fed more often and separately from forage or with higher CP diets (Muller et al., 1980; Röhrmoser et al., 1983).
In a more recent study, the feeding frequency did not affect CH4 production in dairy cows (Crompton et al., 2010). The literature on the effect of feeding rate on animal production is also scarce. In practical conditions, animals consume feed multiple times during a feeding cycle, even if fed once daily. As a result, feeding frequency does not appear to affect feed intake. For example, feeding first lactation dairy cows once or (4) four times a day had no effect on DMI or milk production (Nocek and Braund, 1985). Similarly, Dhiman et al. (2002) did not report any production advantage of feeding lactating dairy cows once or (4) four times daily. In some cases, milk production of dairy cows was reduced with frequent feeding, and the authors attributed this to more frequent handling (Phillips and Rind, 2001). Further discussion of this topic can be found in Hristov and Jouany (2005) and Hall and Huntington (2008).
Precision Feeding and Feed Analyses
In animal nutrition, precision feeding may have different dimensions, but from a practical standpoint and farm sustainability perspective, it refers to matching animal requirements with dietary nutrient supply. Accurate prediction of animal requirements and accurate feed analyses go hand-in-hand with minimizing feed waste, maximizing production, and minimizing GHG emissions per unit of animal product. Precision feeding would likely have an indirect effect on CH4 emission through maintaining a healthy rumen and maximizing microbial protein synthesis, which is essential for maximizing feed efficiency and decreasing CH4 Ei. Much progress in improving animal productivity and reducing CH4 emissions from livestock, specifically Ei, in developing countries can be achieved through proper diet formulation. Garg et al. (2013) documented remarkable progress in animal performance using a program to feed balanced rations to lactating cows and buffaloes in India.
Evaluation of the nutritional status of animals showed that (71%) seventy one percent of the animals, protein, and energy intakes were higher, and for (65%) sixty five percent, Ca and P intakes were lower than the requirements. Balancing the rations significantly improved milk yield by (2) two to (14%) fourteen percent and milk fat by (0.2) point two to (15%) fifteen percent). Feed conversion efficiency, milk N efficiency, and net daily income of farmers also increased as a result of the ration balancing. Therefore, it is of paramount importance that science-based feeding systems and feed analysis are gradually introduced in developing countries with subsistence animal agriculture. This will not only have a measurable economic benefit for the farmer but will also help maximize production and feed utilization and consequently reduce GHG livestock emissions. Accurate analysis of feed composition is a critical step in the precision feeding process.
Even in developed countries with established feed analysis networks, there is still substantial variability in feed analysis among commercial laboratories (Hristov et al., 2010a; Balthrop et al., 2011) and hence the need for standardization of analytical procedures. In intensive dairy systems, daily monitoring of forage, particularly silage DM, can have a profound effect on the precision feeding of the cow for maximum production and profitability. Feed analysis technologies, such as near-infrared reflectance spectroscopy (NIRS), have been developing rapidly since the late 1980s and has been used routinely for quality and component analysis of grain, oilseeds, and forages for the past two decades. The speed and low cost of NIRS analysis make it feasible for producers to buy ingredients based on quality and to formulate rations accurately to meet the nutrient requirements of the animals to minimize over or underfeeding.
Conclusion
There are several potentially useful CH4 mitigation practices available for the livestock sector today. Some CH4 inhibitors, such as BCM, although effective, cannot be recommended for this purpose because of their toxicity or ozone-depleting effect. With other compounds, such as 3NP, more data are needed before any conclusions can be made. For most compounds in this category, there is insufficient long-term in vivo data. Nitrates may be promising CH4 mitigation agents, particularly in low-protein diets that may benefit from NPN supplementation. When nitrates are used, it is critically important that the animals are adequately adapted to avoid nitrate toxicity. More in vivo studies are needed to fully understand the impact of nitrate supplementation on whole-farm GHG emissions (animal, manure storage, and manure-amended soil), animal production, and animal health.
Fumaric and malic acids may reduce CH4 production when applied in large quantities, but most results indicate no mitigating effect. The long-term effects of these compounds have not been established, and costs are likely to prohibit their adoption. Ionophores, through their impact on feed efficiency and reduction in CH4 per unit of feed, would likely have a moderate CH4 mitigating effect in ruminants fed high-grain or mixed grain–forage diets. The effect is dose, feed intake, and diet composition-dependent and is less consistent in ruminants fed pasture. Hydrolyzable and condensed tannins may offer an opportunity to reduce CH4 production, although intake and animal production may be compromised in some, but not all, instances. The agronomic characteristics of tanniferous forages as well as the concentration and structure of the condensed tannins, must be considered when they are discussed as a GHG mitigation option. Based on limited research, tea saponins seem to have CH4 mitigating potential, but more and long-term studies are required before they can be recommended.
Most essential oils or their active ingredients do not reduce CH4 production, and when CH4 production was reduced in vivo, the long-term effect was not established. Limited data indicate EXE may increase feed efficiency and thus indirectly reduce CH4 production; however, inconsistencies in the data question EXE as an effective mitigation practice. There is insufficient evidence of the direct CH4 mitigating effect of yeast and other DFM. However, yeasts appear to stabilize pH and promote rumen function, especially in dairy cattle, resulting in small but relatively consistent responses in animal productivity and feed efficiency and a possible decrease in CH4 Ei. Defaunation of the rumen cannot be recommended as a CH4 mitigation practice. At this point, none of the existing rumen microbial manipulation technologies are ready for practical application. Still, vaccines could be applied to all ruminants, including those with limited human contact, such as sheep and beef animals on pasture.
It is essential to appreciate that vaccines require the host to produce antibodies against some of their microbiome that is part of a symbiotic relationship, enabling ruminant survival on fiber-based diets. To Proceeding to be effective, the vaccines must cover the entire methanogen community and not just some individual taxa because of likely succession of the insensitive populations that can occupy the same (their only) niche. The extent of reductions in methanogenesis may only be (5) five to (10%) ten percent, and the persistence of the effect is unknown. Still, the potential for widespread application makes this an exciting opportunity for future mitigation of CH4 emissions. In the next E-letter, we will present you with the final E-letter of this Series, bringing you more possibilities and knowledge on the subject!
We want to proceed further, more in-depth on this controversial subject. Therefore, please follow us on social media and join us on the (15th) fifteenth of the month for Part (5!) five to learn more about the Mitigation of methane and nitrous oxide emissions. Thereafter, please join us on the (1st) first and (15th) fifteenth of each month for our fortnightly delivery of insightful, informative must-reads from some of the world’s scientific thinkers. Selected by our editors is a collection of current topics with a profound ability for beneficial improvements, guidelines, and process practices. Thank you for reading our publication entirely; please share it with others who also care. We look forward to your comments and having you with us again fortnightly; we will be thrilled in having you with us; thus, we will take your trust in us with great honor and appreciation.
