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Genotype by environment interactions in fertility traits in New Zealand dairy cows

Overview of attention for article published in Journal of Dairy Science, September 2018
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Article details
Title
Genotype by environment interactions in fertility traits in New Zealand dairy cows
Published in
Journal of Dairy Science, September 2018
DOI 10.3168/jds.2017-14195
Pubmed ID
Authors
Abstract

New Zealand's seasonal dairy farming system entails a condensed calving pattern with cows required to conceive within approximately 12 wk of the planned start of calving. This has resulted in strong selection for fertility through culling of nonpregnant cows and relatively strong emphasis on fertility in Breeding Worth, the national breeding objective that drives sire selection. Despite this, average herd-level fertility is highly variable across New Zealand dairy farms. We studied genotype by environment interaction in fertility-related traits, with the goal of improving selection decisions in different fertility environments. We used data from the New Zealand national dairy database, which contains records on 3,743,862 animals. Herds were classified into high-, mid-, or low-fertility categories or environments based on herd average fertility performance, and data were analyzed in 2 different ways. First, we estimated genetic parameters when the fertility trait was defined specifically for each fertility environment to determine the extent to which genetic correlations between high- and low-fertility environments differed from 1 and the extent of changes in genetic variance across environments. Second, we used simple regression to evaluate the impact of ancestral genetic merit for fertility on cow fertility phenotypes to compare the effect of changes in genetic merit on phenotypic performance between fertility environments. The genetic standard deviations of fertility-related traits were 1.5 to 3.6 times higher in low-fertility herds than in high-fertility herds, and the genetic correlations between the same fertility-related traits between the high- and low-fertility environments were moderate to high, albeit with high standard errors. The high standard errors of the correlations reflected the low heritabilities of the traits and potential problems of culling bias, particularly for traits expressed in later parities. Regression analysis revealed that the bottom 30% of herds (in terms of fertility) could achieve more than twice the benefit from selection for fertility than the top 30% of herds. Although our analyses do not support separate genetic evaluations of fertility in the different environments, they indicate that low-fertility herds could benefit more from targeted selection of sires with higher fertility estimated breeding values than from selection based solely on the multitrait national index. Conversely, high-fertility herds could focus their sire selection on traits other than fertility, provided they avoid very low fertility sires.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 13 18%
Student > Master 11 15%
Student > Bachelor 6 8%
Student > Ph. D. Student 6 8%
Student > Postgraduate 5 7%
Other 12 16%
Unknown 20 27%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 34 47%
Veterinary Science and Veterinary Medicine 6 8%
Medicine and Dentistry 3 4%
Biochemistry, Genetics and Molecular Biology 2 3%
Chemical Engineering 1 1%
Other 4 5%
Unknown 23 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 January 2019.
All research outputs
#16,728,456
of 25,385,509 outputs
Outputs from Journal of Dairy Science
#7,113
of 11,138 outputs
Outputs of similar age
#215,353
of 351,777 outputs
Outputs of similar age from Journal of Dairy Science
#134
of 272 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,138 research outputs from this source. They receive a mean Attention Score of 4.5. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 351,777 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 272 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.