Developing breeding schemes to assist mitigation of greenhouse gas emissions

Wall, E.; Simm, G.; Moran, Dominic

doi:10.1017/s175173110999070x

Cited by 181 publications

(167 citation statements)

References 30 publications

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“…This resembles a situation in which for example direct selection on milk yield indirectly improves feed efficiency (Veerkamp, 1998) or reduces methane intensity (i.e. methane emitted per kilogram of milk) from the cows (Bell et al, 2010;Wall et al, 2010). The animals used to update RP were chosen from the same generation as the evaluated individuals.…”

Section: Resultsmentioning

confidence: 99%

Updating the reference population to achieve constant genomic prediction reliability across generations

Pszczoła

Calus

2016

Animal

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The reliability of genomic breeding values (DGV) decays over generations. To keep the DGV reliability at a constant level, the reference population (RP) has to be continuously updated with animals from new generations. Updating RP may be challenging due to economic reasons, especially for novel traits involving expensive phenotyping. Therefore, the goal of this study was to investigate a minimal RP update size to keep the reliability at a constant level across generations. We used a simulated dataset resembling a dairy cattle population. The trait of interest was not included itself in the selection index, but it was affected by selection pressure by being correlated with an index trait that represented the overall breeding goal. The heritability of the index trait was assumed to be 0.25 and for the novel trait the heritability equalled 0.2. The genetic correlation between the two traits was 0.25. The initial RP ( n = 2000) was composed of cows only with a single observation per animal. Reliability of DGV using the initial RP was computed by evaluating contemporary animals. Thereafter, the RP was used to evaluate animals which were one generation younger from the reference individuals. The drop in the reliability when evaluating younger animals was then assessed and the RP was updated to re-gain the initial reliability. The update animals were contemporaries of evaluated animals (EVA). The RP was updated in batches of 100 animals/update. First, the animals most closely related to the EVA were chosen to update RP. The results showed that, approximately, 600 animals were needed every generation to maintain the DGV reliability at a constant level across generations. The sum of squared relationships between RP and EVA and the sum of off-diagonal coefficients of the inverse of the genomic relationship matrix for RP, separately explained 31% and 34%, respectively, of the variation in the reliability across generations. Combined, these parameters explained 53% of the variation in the reliability across generations. Thus, for an optimal RP update an algorithm considering both relationships between reference and evaluated animals, as well as relationships among reference animals, is required.

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Section: Resultsmentioning

confidence: 99%

Updating the reference population to achieve constant genomic prediction reliability across generations

Pszczoła

Calus

2016

Animal

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“…Raw short-term emission estimates can be used for some purposes such as selective animal breeding, where the requirement is for relative emission data but not necessarily DMP. Determining the genetic parameters for enteric methane, with a view to making genetic improvement by direct selection for emission traits (Wall et al, 2010), is an emerging application of short-term emission measures. The speed and simplicity of their application makes them suitable for defining the methane phenotype of the many individual animals required for genetic and genomic improvement of methane traits in ruminants.…”

Section: Resultsmentioning

confidence: 99%

Applicability of short-term emission measurements for on-farm quantification of enteric methane

Hegarty

2013

Animal

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A short term enteric methane emission measurement is not identical to a measure of daily methane production (DMP) made in a respiration chamber (RC). While RC curtail most variation except that from quantity and composition of feed supplied, all shortterm measurements contain additional sources of variation. The points of difference can include measurement time(s) relative to feeding, feed intake before measurement, animal behaviour in selection of diet and level of activity before measurement. For systems where a short-term emission measurement is made at the same time in the daily feeding cycle (e.g. during twice-daily milking) scaling up of short-term emission rates to estimate DMP is feasible but the scaling coefficient(s) will be diet dependent. For systems such as GreenFeed where direct emission rates are measured on occasion throughout day and night, no scaling up may be required to estimate DMP. For systems where small numbers of emission measures are made, and there is no knowledge of prior feed intake, such as for portable accumulation chambers, scaling to DMP is not currently possible. Even without scaling up to DMP, short-term measured emission rates are adequate for identifying relative emission changes induced by mitigation strategies and could provide the data to support genetic selection of ruminants for reduced enteric emissions.Keywords: ruminant, greenhouse gas, enteric emissions, methane, measurement ImplicationsThe capacity to estimate daily methane production (DMP) or relative methane production of ruminants on the basis of short-term emission measurements will enable verification of enteric emission budgets and the development and verification of abatement techniques. This will enable more rapid deployment of abatement strategies and verify the emissions data used in carbon accounting transactions. Short-term emissions measurements are uniquely placed to accelerate methane abatement by genetic selection of livestock, which requires emission data for a large number of animals. IntroductionThe majority of understanding of animal energetics and daily methane production (DMP) has been obtained from indirect calorimetry using open or closed circuit respiration chambers (RC) (Blaxter, 1962;Blaxter and Clapperton, 1965). Increasingly, however, there is a demand to measure ruminant methane emissions in animals within their natural production environment. This demand arises from the need to verify inventories, verify mitigation claims on-farm and measure large numbers of animals to determine the genetic parameters of methane output (Chagunda et al., 2009;McEwan et al., 2012). Although mobile respiration hood systems exist for on-farm use (Kelly et al., 1994), and RC studies using thousands of animals are now being conducted (Arthur et al., 2012), this is extremely slow and expensive. Robinson et al. (2010) identified that short-term measures of methane production were strongly correlated with DMP and can be used to obtain short-term on-farm emission data without need for an RC. This paper summarise...

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“…In contrast, breeding for reduced CH 4 emissions would have a permanent and cumulative effect (Wall et al, 2010). Several studies have shown that CH 4 emissions by ruminants have a genetic component, with heritability in the range of 0.…”

Section: Introductionmentioning

confidence: 99%

Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions

Negussie

Haas

Dehareng

et al. 2017

Journal of Dairy Science

127

121

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Developing breeding schemes to assist mitigation of greenhouse gas emissions

Cited by 181 publications

References 30 publications

Updating the reference population to achieve constant genomic prediction reliability across generations

Updating the reference population to achieve constant genomic prediction reliability across generations

Applicability of short-term emission measurements for on-farm quantification of enteric methane

Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions

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