This fortnightly newsletter was curated and edited by: J.W. Holloway and his Team
Synopsis
Estimates of genetic/phenotypic covariances and economic values for slaughter weight, growth, feed intake and efficiency, and three potential methane traits were compiled to explore the effect of incorporating methane measurements in breeding objectives for cattle and meat sheep. The cost of methane emissions was assumed to be zero (scenario A), A$476/t (based on A$14/t CO2 equivalent and methane’s 100-yr global warming potential [GWP] of 34; scenario B), or A$2,580/t (A$30/t CO2 equivalent combined with methane’s 20-yr GWP of 86; scenario C). Methane traits were methane yield (MY; methane production divided by feed intake based on measurements over 1 d in respiration chambers) or short-term measurements of methane production adjusted for live weight (MPadjWt) in grazing animals, e.g., 40–60 min measurements in portable accumulation chambers (PAC) on 1 or 3 occasions, or measurements for 1 wk using a GreenFeed Emissions Monitor (GEM) on 1 or 3 occasions.
Feed costs included the cost of maintaining the breeding herd and growth from weaning to slaughter. Sheep were assumed to be grown and finished on pasture (A$50/t DM). Feed costs for cattle included 365 d on pasture for the breeding herd and averages of 200 d postweaning grow-out on pasture and 100 d feedlot finishing. The most significant benefit of having methane in the breeding objective for both sheep and cattle was as a proxy for feed intake. For cattle, 3 GEM measurements were estimated to increase profit from 1 round of selection in scenario A (no payment for methane) by A$6.24/animal (from A$20.69 to A$26.93) because of reduced feed costs relative to gains in slaughter weight and by A$7.16 and A$12.09/ animal, respectively, for scenarios B and C, which have payments for reduced methane emissions. For sheep, the improvements were more modest.
Returns from 1 round of selection (no methane measurements) were A$5.06 (scenario A), A$4.85 (scenario B), and A$3.89 (scenario C) compared to A$5.26 (scenario A), A$5.12 (scenario B), and A$4.72 (scenario C) for 1 round of selection with 3 PAC measurements. Including MY in the selection, the index was less profitable because it did not reduce feed costs relative to weight gain. Consequently, for strategies measuring MY but not MPadjWt (and with no estimate of feed intake in the production environment), proportionately greater emphasis was placed on increasing slaughter weight. As a result, the decreases in methane emissions per animal and per unit of feed intake were smaller than for strategies that measured MPadjWt.
Commentary
This limited series of the occasional e-letters are comprised of (2) two articles. They will appear fortnightly and are published during February and March, though it will be accessible through our social media pages.
Analysis
Traits
To determine the value of including methane measurements in the breeding strategy and enhance understanding of the relationships between essential traits, a simplified breeding objective, based on the main drivers of profit and costs, was used. Slaughter weight (Sltwt), as a proxy for production, was assumed to be the primary source of income. Because of the high genetic and phenotypic correlations between slaughter and other postweaning weights, it was assumed that when direct measurements were not available, SltWt could still be included in the analysis based on predictions from earlier weights. Feed costs represent at least 60% of the variable costs of beef (and presumably lamb) production for both feedlot and pasture production systems (Goddard et al., 2011), so daily food intake (DFI) was used as the primary indicator of production costs. Estimates of genetic parameters typically apply to DFI calculated as the mean of at least 35 d of measurement (Archer et al., 1997; Wang et al., 2006).
So the breeding objective trait (representing feed costs) was assumed to be a mean of at least 35 d of DM feed intake measurements in an unrestricted production environment. The breeding objective trait for the cost of methane emissions was assumed to be the long-term daily average methane production (Dmp) of animals raised in a commercial production system. For practical reasons, it was assumed that methane would be measured either as methane yield for 1 d in the RC (RCmy) or as methane production adjusted for weight (mpadjwt), measured on 1 or 3 occasions for 40 to 60 min in PAC (sheep) or 1 wk in the GEM system (cattle). The benefit of repeated measurements depends on the repeatability of the trait, which, for methane, depends on the time interval between measurements. For sheep, repeatability of PAC measurements of MPadjWt in the same week averaged 0.48, whereas the average correlation between 6 repeated measures on the same animals from July 2009 to May 2014 at two different locations using two different measurement techniques was 0.20 (Robinson et al., 2015).
For RC measurements of cattle, the repeatability of DMP on consecutive days was 0.94, compared to 0.27 for measurements more than 60 d apart; the repeatability of MY was somewhat lower: 0.75 for measurements on successive days, declining to 0.21 for measurements 61 to120 d apart and 0.19 for measurements more than 120 d apart (Donoghue et al., 2016). This suggests that multiple PAC or GEM measurements should ideally be some weeks apart and conducted in typical pasture conditions (or over the range of customary pasture conditions) for the production environment. None of the scenarios involve direct measurement of residual feed intake (RFI; the difference between the amount of feed eaten by an animal and what would be expected from its weight and weight gain). The correlated response in RFI to selection for reduced methane emissions was, however, considered to be of interest. The economic value of RFI (over and above any changes to weight and DFI) was assumed to be zero, so including RFI should not directly affect the results for any other trait.
Compiled estimates of means, heritabilities, repeatabilities, and phenotypic SD. Compiled estimates of genetic and phenotypic correlations are shown in the Supplemental Material, together with the sources from which they were derived (see the online version of the article at http://journalofanimalscience.org). No evidence was found of significant differences between cattle and sheep for genetic or phenotypic correlations of methane traits, weight, and DFI, so the same correlation matrices were used for both. Additional sensitivity analyses were conducted using higher estimates of the genetic and phenotypic correlations between DMP and MY (see the Supplemental Material; see the online version of the article at http:// journalofanimalscience.org).
Economic Values of Breeding Objective Traits
Slaughter Weight
A value of A$2.50/kg was chosen as the economic value for the slaughter weight of both sheep and cattle based on a relatively conservative estimate of sale prices.
Feed Intake
Feed costs depend on the production system. An increasing proportion of cattle is feedlot finished, so feed prices for cattle were calculated for 200 d postweaning grow out on pasture, 100 d of grain finishing, and 365 d of pasture for the cow-calf unit until weaning. The effect of a 1 kg/d reduction in DFI was assumed to be the average of the cost of pasture eaten by the calf postweaning, feed intake in the feedlot, and (assuming females will be replaced from the breeding herd) pasture intake of the cow-calf unit. Current calving rates of 0.76 per beef cow (Australian Bureau of Statistics [ABS], 2013) imply an average of 480 cow-days of pasture intake per calf, so the assumption of 365 d per cow-calf unit is somewhat of an underestimate allowing some discounting for the time delay in realizing the reductions in DFI as females are replaced.
Feedlot feed costs were assumed to be A$300/t DM. The cost of pasture was assumed to be A$50/t DM, a small increase in the cost used by Cottle et al. (2009). Sheep were assumed to be raised entirely on pasture. The same cost of A$50/t DM was used, assuming 200 d feed intake of the lamb postweaning and 365 d of pasture intake for the ewe-lamb unit and an average of 1 lamb weaned per ewe, slightly higher than the current value of 0.97 for meat sheep (ABS, 2013), but no discounting for the time required for females to be replaced.
Daily Methane Production
Methane emissions (which are highly correlated with feed intake) were calculated for the same time periods as feed intake: 365 d for the ewe-lamb unit plus 200 d postweaning for lambs (a total of 565 d) and 665 for beef (365 d for the cow-calf unit and 300 d postweaning). Three different economic scenarios were used for methane: zero (scenario a), A$476/t of CH4 emissions based on A$14/t of CO2 equivalent (the price paid under the Australian government’s Direct-Action emissions reductions policy), and methane’s 100-yr global warming potential (Gwp) of 34 (scenario B; Myhre et al., 2013), and a higher estimate (A$2,580/t) based on methane’s 20-yr GWP of 86 (Myhre et al., 2013) and a cost of A$30/t of CO2 equivalent (scenario C). The latter was considered a plausible upper value because of the agreement to keep the global temperature increase well below 2°C (UNFCCC, 2015).
The estimated increase in global temperatures to 2012 was 0.85°C (Intergovernmental Panel on Climate Change, 2013); temperatures in 2015 were 1°C above preindustrial levels (WMO, 2016). Projections suggest that at current levels of emissions, the UNFCCC target is likely to be exceeded within 17 to 30 yr and that it will not be possible to limit the rise to 2°C just by reducing CO2 emissions (UNEP and WMO, 2011); an increased focus on reducing emissions of more intense but shorter-lived climate pollutants (SlCp) will be necessary to keep within the agreed-upon limit. Rapid increases in the price of SLCP emissions are therefore quite possible as temperatures get closer to the agreed-upon limit and governments consider new policies to meet the target.
Measurement Strategies
For both cattle and sheep, four measurement strategies were considered: 1) weight but no methane measurements (wt), 2) weight plus MY in the RC (my), 3) weight plus one measurement of MPadjWt (40 to 60 min in a PAC or 7 d of GEM measurement; m1), and 4) weight plus three measurements of MPadjWt (m3). The measurement strategies were evaluated using MTINDEX software (van der Werf, 2015) to determine the index weights for each measured trait to maximize the returns per animal when animals are sold for slaughter. Although this relatively crude evaluation ignores many overhead costs, e.g., the cost of breeding and raising replacement females, it provides a useful comparison of the relative merits of the different measurement strategies. The 4 strategies were compared for the three cost scenarios for methane emissions (scenarios A, B, and C above). If under cost scenario C, the most profitable strategy for MY, M1, or M3 led to increased methane emissions per animal, the desired gains approach (scenario D) of not allowing methane emissions per animal to increase was also considered.
In all cases, it was assumed that all animals would be measured, as well as their sires, dams, and siblings. Cattle were assumed to have 20 half-siblings, and sheep were assumed to have an average of 0.5 full and 30 half-sibs. Responses to Selection. The genetic gains and total economic return for each trait were calculated by MTINDEX from the compiled estimates of genetic parameters and economic values. First, estimated genetic and phenotypic covariance matrices are checked for positive definiteness. If required, “bending” (Hayes and Hill, 1981) is used to transform them into positive-definite matrices (estimates before and after bending are provided in the Supplemental Material; see the online version of the article at http:// journalofanimalscience.org). The economic return is expressed as an index I = b′X of the phenotypic measurements X.
Index weights b are calculated as b = p−1Gv, where G = the genetic covariance matrix of the traits in X, p = the phenotypic covariance matrix, and v = the economic value of a unit increase in the traits in the selection index. The phenotypic SD of the index I is, therefore, σI = sqrt(b′Pb), and the response to selection is iσO for i = selection intensity and σO = sqrt(v′Gv) = genetic SD of the objective (i.e., the SD of its true breeding value). The change in individual traits can be calculated as ib′Gi/σI for Gi = ith column of G. The values reported in the tables are estimated changes per animal for a single round of selection at intensity i = 1, equivalent, for example, to selecting the best 20% of males and 63% of females or the best 10% of males and 87% of females.
Results
Cattle
If methane emissions have zero cost and weight at slaughter age is the only measurement used to select cattle, the increased value of the saleable product (A$42.35 for an extra 16.9 kg at A$2.50/kg) is offset by increased DFI (0.37 kg per animal per day, costing A$21.65), leading to an overall improvement in profitability of A$20.69/animal. The predicted increase in CH4 emissions is 3.06 g per animal per day. When methane is measured, the optimum strategy for scenario A (no payment for methane) has a smaller increase in a saleable product but much smaller increases in feed intake and methane, 0.19 kg DFI and 1.26 g CH4 per animal per day (1 wk of GEM measurements) or 0.10 kg DFI and 0.28 g CH4 per animal per day (3 sets of GEM measurements), resulting in overall improvements in profitability from selection to A$24.50/ animal (1 wk GEM measurements) or A$26.93/animal (3 sets of GEM measurements per animal).
If producers are financially rewarded for reducing methane emissions, the optimal strategy depends on the price of methane. For scenario B (methane price of A$476/t), emissions per animal increase but by only 1.08 g/d if methane is measured for 1 wk and 0.04 g/d if there are 3 GEM measurement sessions. For scenario C (methane price of A$2,580/t) but no methane measurements, the increased emissions of 3.06 g per animal per day reduces the return from increased slaughter weight by A$5.25. However, with one 7-d GEM measurement session, the optimal selection strategy results in very little increase in DMP (0.20 g per animal per day), and with 3 GEM measurement sessions, DMP is predicted to decline by 0.97 g per animal per day. In addition, despite the predicted 9.5 kg increase in slaughter weight, DFI is predicted to fall by 36 g per animal per day, reducing feed costs by A$2.12/animal.
Sheep
A similar pattern is evident for meat sheep, except that the returns per animal are lower. Slaughter weight was assumed to have the same heritability (40%) for both cattle and sheep, but the lower phenotypic SD of 5.92 for sheep leads to a smaller weight increase (2.75 kg in the absence of any methane measurements) than for cattle. Sheep were assumed to be pasture finished, so the economic weights on feed costs were lower than for cattle (assumed to have 100 d of feedlot finishing). In all cases, the most profitable selection strategy increases feed intake and DMP per animal, ranging from 0.80 g CH4/d if methane is not measured to 0.18 g CH4/d for 3 PAC measurements in scenario C. Depending on the price paid for stud rams. Whether producers receive payments for reduced methane emissions, it could still be worthwhile to measure and breed for reduced methane emissions, which, at the same time, will help limit the increase in feed costs. For measurement strategy MY, scenario D (no rise in CH4/animal, CH4 cost of A$2,580/t) generated substantially lower returns than scenario C (A$1.54 for scenario D vs. A$3.91 for scenario C) but only small differences for M1 (A$4.16 for scenario D vs. A$4.45 for scenario C) and M3 (A$4.67 for scenario D vs. A$4.72 for scenario C).
Sensitivity Analysis
The sensitivity analysis considered the effect of higher genetic and phenotypic correlations for RCMY with DMP (see the Supplemental Material; see the online version of the article at http://journalofanimalscience. org). The most significant differences were for strategy MY cost scenario C, in which 1 round of selection for cattle generated a total return of A$15.62/animal (compared to A$15.50 for the same strategy/cost scenario; with increased DMP of 2.59 g per animal per day (compared to 2.96) and decreased MY of 0.08 g/kg (compared 0.03 g/kg;. These results can be contrasted with strategy M1 (1 wk of GEM measurements) in cost scenario C, which generated an identical reduction in MY (0.08 g/kg;) but much more significant improvements in total return (A$23.22) and a much lower increase in DMP (0.20 g per animal per day;). For sheep, the alternative parameters for strategy MY scenario C increased total return by A$3.94/ animal (compared to A$3.91 for the original parameters;) and increased CH4 by 0.72 g per animal per day (original parameters: 0.77) with a decreased MY of 0.05 g/kg (original parameters: 0.03).
These results can be contrasted with strategy M1 in the same cost scenario, which generated an almost identical reduction in MY (0.05 g/kg;) but a higher return of A$4.45/ animal and a smaller increase in DMP of 0.37 g per animal per day.
Conclusion
The world has always been concerned with its future. That concern has become endemic in this era. Global climate change is a new term introduced in our generation. As Pogo famously said, “we have found the enemy, and it is us”. Unfortunately, the almost miracle accomplished by ruminant animals in converting lignified materials into healthful, delicious foods also requires the production of methane and other byproducts that have been condemned as contributors to global climate change. Therefore, an essential extrinsic character of red meat in many markets is how much damage to the environment and our collective future on the planet did the production of the red meat contribute. This chapter presents a review of the scientific literature concerning the science underlying ruminal fermentation and the consequences of fermentation on the environment as well as the discovery of technologies to alleviate these consequences.
Among the first results obtained to date, biochemical or genomic data converge on the idea that cattle with high muscle growth potential have faster and more glycolytic muscles, which is favorable for the tenderness of the meat produced but unfavorable. for its taste (due to lower intramuscular lipid contents). The regulation of new indicators from genomics approaches is indeed comparable to that of muscle characteristics already known. The sector must understand the interest of new genomics techniques and appropriate them to participate fully in the development of the tools that will be useful to it. In the next E-letter, we will discuss the final results of this study and how we interpreted them! 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 (2!) two to learn more about the environmental impact of beef production.
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.
