Genomic testing interacts with reproductive surplus in reducing genetic lag and increasing economic net return

Hjortø, Line; Ettema, Jehan Frans; Kargo, Morten; Sørensen, A.C.

doi:10.3168/jds.2014-8401

Cited by 26 publications

(44 citation statements)

References 15 publications

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“…Depending on the considered genotyping cost and the considered economic value, our results suggest that use of GS for replacement is beneficial in more scenarios, compared with other studies (De Roos, 2011;Pryce and Hayes, 2012;Weigel et al, 2012;Hjortø et al, 2015). Our results are somewhat different from those of others for 2 main reasons.…”

Section: Discussioncontrasting

confidence: 84%

“…As pointed out by Hjortø et al (2015), this strategy may be suboptimal, if the best animals based on parent average are among those that would be selected for replacement based on genomic selection. An alternative strategy is to genotype heifers that have parent averages centered around the level of parent average above which heifers would be selected for replacement, which may increase the benefit of genotyping for replacement by reducing the total genotyping costs (Hjortø et al, 2015). Whether this leads to a further increase of the revenues due to genotyping likely depends on accuracy achieved for selection based on parent average.…”

Section: Discussionmentioning

confidence: 98%

See 1 more Smart Citation

Evaluation of genomic selection for replacement strategies using selection index theory

Calus

Bijma

Veerkamp

2015

Journal of Dairy Science

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Our objective was to investigate the economic effect of prioritizing heifers for replacement at the herd level based on genomic estimated breeding values, and to compute break-even genotyping costs across a wide range of scenarios. Specifically, we aimed to determine the optimal proportion of preselection based on parent average information for all scenarios considered. Considered replacement strategies include a range of different selection intensities by considering different numbers of heifers available for replacement (15-45 in a herd with 100 dairy cows) as well as different replacement rates (15-40%). Use of conventional versus sexed semen was considered, where the latter resulted in having twice as many heifers available for replacement. The baseline scenario relies on prioritization of replacement heifers based on parent average. The first alternative scenario involved genomic selection of heifers, considering that all heifers were genotyped. The benefits of genomic selection in this scenario were computed using a simple formula that only requires the number of lactating animals, the difference in accuracy between parent average and genomic selection (GS), and the selection intensity as input. When all heifers were genotyped, using GS for replacement of heifers was beneficial in most scenarios for current genotyping prices, provided some room exists for selection, in the sense that at least 2 more heifers are available than needed for replacement. In those scenarios, minimum break-even genotyping costs were equal to half the economic value of a standard deviation of the breeding goal. The second alternative scenario involved a preselection based on parent average, followed by GS among all the preselected heifers. It was in almost all cases beneficial to genotype all heifers when conventional semen was used (i.e., to do no preselection). The optimal proportion of preselection based on parent average was at least 0.63 when sexed semen was used. Use of sexed semen increased the potential benefit of using GS, because it increased the room for selection. Critical assumptions that should not be ignored when calculating the benefit of GS are (1) a decrease in replacement rate can only be achieved by increasing productive life in the herd, and (2) accuracies of selection should be used rather than accuracies of estimated breeding values based on the prediction error variance and base-generation genetic variance, because the latter lead to underestimation of the potential of GS.

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Section: Discussioncontrasting

confidence: 84%

Section: Discussionmentioning

confidence: 98%

Evaluation of genomic selection for replacement strategies using selection index theory

Calus

Bijma

Veerkamp

2015

Journal of Dairy Science

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“…; Hjorto et al . ). The situation remains profitable if costs are higher, but at a longer horizon, likely not acceptable by the farmer.…”

Section: Female Genotyping: a Strategic Choicementioning

confidence: 97%

“…Genotyping profitability depends on genotyping cost. It was demonstrated that a positive return due to extra genetic gain is expected after 4 years, that is after the first or second lactation of the progeny if genotyping costs <40€ to 50€ according to economic conditions (Boichard et al 2013;Calus et al 2013;Pryce et al 2012a;Hjorto et al 2015). The situation remains profitable if costs are higher, but at a longer horizon, likely not acceptable by the farmer.…”

Section: For Herd Managementmentioning

confidence: 99%

Sustainable dairy cattle selection in the genomic era

Boichard¹,

Ducrocq²,

Fritz³

2015

J Animal Breeding Genetics

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Genomic selection offers considerable flexibility to increase genetic trends in dairy cattle breeding. It is also an opportunity for more sustainable breeding, in terms of breeding goal and genetic variability. With a shorter generation interval, there is a big risk of increasing inbreeding if semen dissemination policy of elite bulls is not changed. However, using a large number of young bulls as service bulls and bull sires is a solution for both maximizing genetic trend while reducing inbreeding trend. Female genotyping is a key challenge for within-herd selection but, more importantly, for selection of new traits and for replacement of current reference populations based upon progeny-tested bulls. Genomic selection also opens new avenues for more customized breeding across herds or production systems. A big challenge is to reduce the dependency of genomic predictions on relationship between candidates and the reference population. A strong effort is presently dedicated to integrating genome sequence information into predictions, to improve their accuracy and persistency. In the longer term, further customization of selection will be possible by accounting for G × E interactions. Important developments are also necessary to decrease loss of favourable alleles through genetic drift.

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“…One approach is the use of complex (usually stochastic) simulations to model the impact of genotyping across all areas of the farm. For example, Hjortø et al (2015) reported on the change in operational return, which encompasses all sale income minus variable costs of cows and young stock, when exploring adoption of genotyping. This modelling approach also facilitated the use of genomic information multiple times over the animal's lifetime.…”

Section: Micro-valuation Example 2: Cost-benefit Analysesmentioning

confidence: 99%

Demonstrating the value of herd improvement in the Australian dairy industry

Newton

Axford²,

et al. 2021

Anim. Prod. Sci.

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Herd improvement has been occurring since the domestication of livestock, although the tools and technologies that support it have changed dramatically. The Australian dairy industry tracks herd improvement through a range of approaches, including routine monitoring of genetic trends and farmer usage of the various tools and technologies. However, a less structured approach has been taken to valuing the realised and potential impacts of herd improvement. The present paper aims to demonstrate the value of herd improvement, while exploring considerations for undertaking such a valuation. Attractive value propositions differ among and within dairy stakeholder groups. While broad-scale valuations of genetic trends and industry progress are valued by government and industry, such valuations do not resonate with farmers. The cumulative nature of genetic gain and compounding factor of genetic lag means that long timeframes are needed to fully illustrate the value of genetic improvement. However, such propositions do not align with decision-making timeframes of most farming businesses. Value propositions that resonate with farmers and can lead to increased uptake and confidence in herd improvement tools include smaller scale cost–benefit analyses and on-farm case studies developed in consultation with industry, including farmers. Non-monetary assessments of value, such as risk and environmental footprint, are important to some audiences. When additionality, that is, the use of data on multiple occasions, makes quantifying the value of the data hard, qualitative assessments of value can be helpful. This is particularly true for herd recording data. Demonstrating the value of herd improvement to the dairy industry, or any livestock sector, requires a multi-faceted approach that extends beyond monetary worth. No single number can effectively capture the full value of herd improvement in a way that resonates with all farmers, let alone dairy stakeholders. Extending current monitoring of herd improvement to include regular illustrations of the value of the tools that underpin herd improvement is important for fostering uptake of new or improved tools as they are released to industry.

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Genomic testing interacts with reproductive surplus in reducing genetic lag and increasing economic net return

Cited by 26 publications

References 15 publications

Evaluation of genomic selection for replacement strategies using selection index theory

Evaluation of genomic selection for replacement strategies using selection index theory

Sustainable dairy cattle selection in the genomic era

Demonstrating the value of herd improvement in the Australian dairy industry

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