Search

Dairy farmers are motivated to ensure cows become pregnant in an optimal and timely manner. Although timed artificial insemination (TAI) is a successful management tool in dairy cattle, it masks an animal's innate fertility performance, likely reducing the accuracy of genetic evaluations for fertility traits. Therefore, separating fertility traits based on the recorded management technique involved in the breeding process or adding the breeding protocol as an effect to the model can be viable approaches to address the potential bias caused by such management decisions. Nevertheless, there is a lack of specificity and uniformity in the recording of breeding protocol descriptions by dairy farmers. Therefore, this study investigated the use of 8 supervised machine learning algorithms to classify 1,835 unique breeding protocol descriptions from 981 herds into the following 2 classes: TAI or other than TAI. Our results showed that models that used a stacking classifier algorithm had the highest Matthews correlation coefficient (0.94 ± 0.04, mean ± SD) and maximized precision and recall (F1-score = 0.96 ± 0.03) on test data. Nonetheless, their F1-scores on test data were not different from 5 out of the other 7 algorithms considered. Altogether, results presented herein suggest machine learning algorithms can be used to produce robust models that correctly identify TAI protocols from dairy cattle breeding records, thus opening the opportunity for unbiased genetic evaluation of animals based on their natural fertility.

Oral Presentation at the 2020 ADSA Annual Meeting (online). The objectives of this study were to identify the temperature-humidity index (THI) thresholds where heat load starts to affect production traits (milk, fat, and protein yield) in the first 3 lactations of Holstein cows, and to assess potential differences in heat stress in Quebec and Ontario. A total of 2.1 million test-day records from 167,620 Ontario cows and 3.1 million records from 225,104 Quebec cows spanning an 11-yr period (2008 – 2018) were used. The climatic data consisted of hourly measurements of ambient temperature and relative humidity. In total 58 weather stations were located within a maximum distance of 20km from each herd. A k-means cluster analysis was performed within each THI >75 to exclude herds that may have some type of cooling system. A linear model was fitted to adjust the phenotypes, which were then plotted against the THI levels for each parity. To identify the THI threshold at which milk production starts to decline, a segmented polynomial was used to describe the shape of the curve of the relationship between production and THI values. During the summer, the average THI was 69 and 68 in Ontario and Quebec, respectively. Two heat stress thresholds were identified for milk yield and protein yield, one being associated with a low decline in the yield and the other one associated with sharper decline. For both provinces, the second threshold was reached at about THI = 79 for both milk and protein yields, while the first threshold was reached at about a THI = 64 – 72 for milk yield and THI = 57 – 64 for protein yield, depending on the province and parity. The expected reduction in milk yield after a THI ~79 was −0.40 kg/d and −0.19kg/d in Ontario and Quebec, respectively. The same features for protein yield were −16.2 g/d and −9.7 g/d. For fat yield, the expected reduction in yield after a THI ~57 was −3.3g/d and −2.6g/d for Ontario and Quebec, respectively. Therefore, the estimated rate decline differed between the 2 provinces, which may be related to the higher percentage of tie stall barns in Quebec (92%) compared with Ontario (

Oral Presentation at the 2020 ADSA Annual Meeting (online). Breeding programs across the globe are shifting their selection goals from production-driven to more balanced indexes that include traits of direct and indirect economic significance, such as conformation, fertility and health. In Canada, for instance, changes were made to better reflect producers and market needs in the wake of improved accuracy of genetic and genomic evaluations and national efforts regarding data recordings. In 2015 a new national profit index, Pro$, was introduced to maximize production yields, while maintaining functional and conformation traits, taking into consideration the accumulated profit to 6 years of age of proven sire’s daughters. The Canadian classification system comprises several linear and nonlinear descriptive traits and defective characteristics that are taken into account to calculate points, which are distributed in 4 different scorecard sections: Mammary System (MS), Feet & Legs, Dairy Strength (DS) and Rump. Understanding the contribution of each of these traits to a monetary index, such as Pro$, would help producers make culling and mating decisions ultimately resulting in more profitable herds. Therefore, the present study used statistical regression and principal component analysis to look at the overall and individual contribution of 23 classification traits to the Pro$ index using deregressed Estimated Breeding Values from 9,292 proven bulls (Lactanet). The total variance explained by the full model was 0.66, and out of the traits included in the model, Dairy Capacity contributed the most to the increase in Pro$. Stepwise backward regression analysis indicated that all traits should remain in the full model. Principal component analysis showed that the vast majority of traits contributed positively to Pro$ values. Traits from the MS contributed the most to the increase in Pro$ values, whereas Thurl Placement and Rump Angle did not seem to have a sizeable impact on Pro$. Dairy Research Cluster 3 – Canada, Canadian Agricultural Partnership, Lactanet, Center for Genetic Improvement of Livestock (CGIL)

The Joint ADSA/Interbull Breeding and Genetics Symposia titled “Ten Years of Genomic Selection” and “Data Pipelines for Implementation of Genomic Evaluation of Novel Traits” were held at the 2019 ADSA Annual Meeting, from June 23 to 26, in Cincinnati, Ohio. The objective of the first symposium was to provide a broad overview of 10 years of genomic selection in dairy cattle. In 2009, genomic evaluations were first implemented in the United States and Canada, followed over time by evaluations in many other countries. The rapid uptake of genomic selection has had a dramatic effect on the dairy industry. This symposium highlighted the development and impact of this implementation in all dairy breeds and outlined new developments and future scenarios. The objective of the second symposium was to provide an international view of recent worldwide advances on the development of new data pipelines, with the overall objective of implementing genetic evaluations for novel traits in the era of genomics. The advent of genomics has created an opportunity to focus on and select for expensive traits, which was not feasible with traditional selection. This symposium provided various examples of national and international initiatives to pool data across countries or organizations to exploit the potential of accurate genomic evaluation for novel and expensive traits

In Canada, reproductive disorders known to affect the profitability of dairy cattle herds have been recorded by producers on a voluntary basis since 2007. Previous studies have shown the feasibility of using producer-recorded health data for genetic evaluations. Despite low heritability estimates and limited availability of phenotypic information, sufficient genetic variation has been observed for those traits to indicate that genetic progress, although slow, can be achieved. Pedigree- and genomic-based analyses were performed on producer-recorded health data of reproductive disorders, including retained placenta (RETP), metritis (METR), and cystic ovaries (CYST) using traditional BLUP and single-step genomic BLUP. Genome-wide association studies and functional analyses were carried out to unravel significant genomic regions and biological pathways, and to better understand the genetic mechanisms underlying RETP, METR, and CYST. Heritability estimates (posterior standard deviation in parentheses) were 0.02 (0.003), 0.01 (0.004), and 0.02 (0.003) for CYST, METR, and RETP, respectively. A moderate to strong genetic correlation of 0.69 (0.102) was found between METR and RETP. Averaged over all traits, sire proof reliabilities increased by approximately 11 percentage points with the incorporation of genomic data using a multiple-trait linear model. Biological pathways and associated genes underlying the studied traits were identified and will contribute to a better understanding of the biology of these 3 health disorders in dairy cattle.

The effects of 2 deleterious recessive haplotypes on reproduction performance of Ayrshire cattle, Ayrshire Haplotype 1 (AH1) and Ayrshire Haplotype 2 (AH2), were investigated in Canadian Ayrshire cattle. We calculated their phenotypic effects on stillbirth (SB) rate and 56-d nonreturn rate (NRR) by estimating the interaction of service sire carrier status with maternal grandsire carrier status using the official Canadian evaluation models for those 2 traits. The interaction term included 9 subclasses for the 3 possible statuses of each bull: haplotype carrier, noncarrier, or not geno-typed. For AH1, 394 carriers and 1,433 noncarriers were available, whereas 313 carriers and 1,543 noncarriers were available for the AH2 haplotype. The number of matings considered for SB was 34,312 for heifers (first parity) and 115,935 for cows (later parities). For NRR, 49,479 matings for heifers and 160,528 for cows were used to estimate the haplotype effects. We observed a negative effect of AH1 on SB rates, which was 2.0% higher for matings of AH1-carrier sires to dams that had an AH1-carrier sire; this effect was found for both heifers and cows. However, AH1 had small, generally nonsignificant effects on NRR. The AH2 haplotype had a substantial negative effect on NRR, with 5.1% more heifers and 4.0% more cows returning to service, but the effects on SB rates were inconsistent and mostly small effects. Our results validate the harmful effects of AH1 and AH2 on reproduction traits in the Canadian Ayrshire population. This information will be of great interest for the dairy industry, allowing producers to make mating decisions that would reduce reproductive losses.

International audience

The success and sustainability of a breeding program incorporating genomic information is largely dependent on the accuracy of predictions. For low heritability traits, large training populations are required to achieve high accuracies of genomic estimated breeding values (GEBV). By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (ssGBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. The aim of this study was to compare the accuracy and bias of genomic predictions for various traits in Canadian Holstein cattle using ssGBLUP and multi-step genomic BLUP (msGBLUP) under different strategies, such as (1) adding genomic information of cows in the analysis, (2) testing different adjustments of the genomic relationship matrix, and (3) using a blending approach to obtain GEBV from msGBLUP. The following genomic predictions were evaluated regarding accuracy and bias: (1) GEBV estimated by ssGBLUP; (2) direct genomic value estimated by msGBLUP with polygenic effects of 5 and 20%; and (3) GEBV calculated by a blending approach of direct genomic value with estimated breeding values using polygenic effects of 5 and 20%. The effect of adding genomic information of cows in the evaluation was also assessed for each approach. When genomic information was included in the analyses, the average improvement in observed reliability of predictions was observed to be 7 and 13 percentage points for reproductive and workability traits, respectively, compared with traditional BLUP. Absolute deviation from 1 of the regression coefficient of the linear regression of de-regressed estimated breeding values on genomic predictions went from 0.19 when using traditional BLUP to 0.22 when using the msGBLUP method, and to 0.14 when using the ssGBLUP method. The use of polygenic weight of 20% in the msGBLUP slightly improved the reliability of predictions, while reducing the bias. A similar trend was observed when a blending approach was used. Adding genomic information of cows increased reliabilities, while decreasing bias of genomic predictions when using the ssGBLUP method. Differences between using a training population with cows and bulls or with only bulls for the msGBLUP method were small, likely due to the small number of cows included in the analysis. Predictions for lowly heritable traits benefit greatly from genomic information, especially when all phenotypes, pedigrees, and genotypes are used in a single-step approach.

International audience

International audience

International audience