Hills Ideal Banace Teats Beef & Sweet Potatoes

• Journal Listing
• J Anim Sci
• v.97(11); 2019 November
• PMC6827401

J Anim Sci. 2019 November; 97(11): 4445–4452.

Evolution of optimal genetic evaluations for teat and udder structure in Canadian Angus cattle

Kajal Devani

¹ Section of Product Creature Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, Canada

Tiago Southward Valente

² Department of Agricultural, Food and Nutritional Science, Academy of Alberta, Edmonton, Canada

John J Crowley

² Department of Agronomical, Food and Nutritional Science, University of Alberta, Edmonton, Canada

³ AbacusBio International Ltd, Roslin Innovation Centre, East Bush, Midlothian, Edinburgh, UK

Karin Orsel

¹ Department of Production Creature Health, Kinesthesia of Veterinary Medicine, University of Calgary, Calgary, Canada

Received 2019 May 28; Accepted 2019 Oct 3.

Abstruse

Despite their heritability and influence on female productivity, there are currently no genetic evaluations for teat and udder structure in Canadian Angus cattle. The objective of this study was to develop optimal genetic evaluations for these traits in the Canadian Angus population. Guidelines recommended by Beef Improvement Federation (BIF) were used to score teat and udder structure in 1,735 Canadian Angus cows from 10 representative herds. Cows scored ranged in parity from 1 to xiii; however, >70% of cows were parity ≤4. Scores ranged from 1 (large, canteen shaped) to 9 (very small) for teats and from 1 (very pendulous) to 9 (very tight) for udders. Consistent with parity distribution, >70% of teat and udder scores were ≥six. Teat and udder scores (TS_nine and US_nine, respectively) were modeled using a multiple trait fauna model with random effects of contemporary group (herd-year-flavor) and condiment genetic result, and fixed effects of breed, parity group, and days between calving and scoring. To test expert versus poor structure, a binary classification of ane or 2 (TS₂, U.s.₂) [comprised of scores i to 5 = i (poor construction) and scores 6 to ix = ii (skillful structure)] was created. Further, to assess the impact of group less frequently observed poor scores, a 1 to vii scale (TS₇, US_vii) was created by combining teat and udder scores ane to 3. Analyses for teat and udder scores on scales TS₉, The states₉, TS₇, The states₇, and TS₂, Us₂ were compared. In addition, both threshold and linear fauna models were used to gauge variance components for the traits. Data treatment and models were evaluated based on correlation of resulting estimated breeding value (EBV) with corrected phenotypes, Spearman's rank correlation coefficient, average EBV accuracies (r), and deviance data criteria (DIC). TS₉, U.s.₉ scales for teat and udder scores and linear models performed all-time. Estimates of heritability (SE) for teat and udder score were 0.32 (0.06) and 0.fifteen (0.04), respectively, indicating these traits were moderately heritable and that genetic improvement for teat and udder scores was possible. Estimates of phenotypic and genotypic correlations for teat and udder score were 0.46 (0.02) and 0.71 (0.09), respectively. Estimates of genotypic correlations with nascence weight (BW), weaning weight (WW), and yearling weight (YW), ranged from −0.04 (0.10) to −0.xx (0.12), verifying the importance of selecting for improved teat and udder score as individual traits, aslope performance traits.

Keywords: beefiness cattle, categorical traits, moo-cow longevity, genetic selection, heritability, udder score

Introduction

Poor teat and udder structure have been associated with production inefficiencies (Arnold et al., 2017) through increased mastitis (Persson Waller et al., 2014) and early alternative of cows (Arthur et al., 1992; Riley et al., 2001). In addition, poor teat and udder construction can impact calf survival and growth (Haggard et al., 1983; Wittum and Perino, 1995; McGee and Earley, 2018) through delayed teat finding and suckling (Kruse, 2010; Bunter et al., 2013) and mastitis-associated decreased milk yield and quality (Paape et al., 2000; Contreras et al., 2015).

Genetic evaluations are evolving to include traits that impact production efficiencies and animal health and welfare (Miller, 2018). In addition, the Beefiness Comeback Federation (BIF) (2016) recommended scoring for teat and udder construction, and selecting for tight udder pause and small-scale, symmetrical teats. Equally teat and udder structure deteriorate with age (Smith et al., 2017), early identification of cows that maintain constructive structure would be benign. Previous estimates of heritability for teat and udder score in Holstein, Hereford, and Brahman composites ranged from 0.xiv to 0.49 (Boettcher et al., 1998; Bunter and Johnston, 2014; Bradford et al., 2015) indicating opportunity to select for improved mammary structure.

Although teat and udder score are usually considered as linear traits, Ramirez-Valverde et al. (2001) suggested threshold handling of type traits may more accurately predict genetic merit. In improver, Sapp et al. (2003) suggested that score scales may exist complanate. We assessed these suggestions with the aim to optimize genetic evaluations. Toward the development of a genetic evaluation for teat and udders in Canadian Angus, our objectives were to: 1) identify the most constructive data treatment (scoring scales) and models; ii) judge (co)variance components for teat and udder scores; and three) guess genetic and phenotypic correlations of teat and udder scores with functioning traits.

Materials and Methods

Collection of teat and udder scores was done in accordance with the Canadian Lawmaking of Practise for the intendance and handling of subcontract animals. All procedures involving cattle were reviewed and approved past the University of Calgary Fauna Care Committee (Protocol AC16-0218).

Herd Pick

Ten Canadian Angus moo-cow herds were recruited to participate, based on previously expressed involvement in inquiry and sampling convenience factors (due east.g., location and herd size). Both Black Angus and Blood-red Angus cattle were included, every bit the Canadian Angus herdbook includes both. To be eligible to participate, herds were required to have: 1) a handling facility through which they could process all their cows to enable phenotyping; and 2) have consummate pedigree and performance records at the Canadian Angus Association (CAA).

Teat and Udder Scores

Using BIF scoring guidelines, ii,912 Canadian Angus cows, across x herds, were scored (only one time) during calving seasons from 2014 to 2017, by 1 of iii trained scorers. From parallel scoring during grooming, inter-scorer agreement was 80% (kappa coefficient = 0.78). The BIF scoring system ranged from 1 to nine, where the smallest teats and tightest udders were both assigned a score of 9, and large bottle-shaped teats and pendulous udders that had lost support from suspensory ligaments were both assigned a score of ane (Beef Improvement Federation, 2016). Every bit suggested by the guidelines, all bred females in each herd were scored, recording teat and udder on the weakest quarter of each moo-cow. A divergence from guidelines, that recommend scoring within 24 h mail-calving, occurred every bit teat and udder scores were observed at producer convenience. Hence, scoring time relative to calving engagement ranged from −121 days to +128 days. Of the 2,912 Canadian Angus cows, 617 were omitted for existence scored prior to calving and a further, 560 cows were removed due to a lack of age, parity, or dogie information.

Later on quality control of phenotypic data ready, one,735 teat and udder scores remained with means (SD) of 6.68 (1.49) and half-dozen.56 (1.61), respectively. Consistent with Beard et al. (2018), we created a threshold (binary) scale (TS₂, US₂) of 1 to 2 in which scores from 1 to five were designated equally class 1 and scores from 6 to 9 were designated every bit class two. To appraise condensing less frequently observed teat and udder scores, scores ane to three were designated into 1 grouping creating a ane to 7 scale (TS_seven, Us₇). Thus, optimal information treatment analyses for teat and udder scores were carried out on all 3 scales (TS₉, US₉, TS_seven, US₇, and TS₂, US₂).

Pedigree and Performance Data:

A 4-generation pedigree file for 1,735 cows and their calves was extracted from the CAA database. Thus, the pedigree file consisted of 52,024 animals, including 8,199 dams and iii,227 sires. Additional performance data on cows and their calves, including date of birth, parity, sex activity, birth weight (BW), weaning weight (WW), and yearling weight (YW) were also available. It was noteworthy that BW, WW, and YW data extracted from the CAA database were already adapted for age of dam (BW and WW), and historic period at measure (WW and YW) every bit per BIF (2016). All WW nether 136 kg and YW over 635 kg were removed. Gimmicky groups (CG) (defined as herd of origin, year, and season of nascence; with 4 seasons January to March, April to June, July to September, and lastly Oct to December) with less than 3 animals and those with no variation were excluded. The 275 CG for BW contained an average of 36 animals (min = iii; max = 676), the 259 CG for WW contained an average of 31 animals (min = 3; max = 480), and the 187 YW CG averaged 26 animals (min = three; max = 248). Ultimately, 9,916 BW, 8,110 WW, and 4,828 YW measures were available for further analyses.

Data Analyses

An animal model was used to estimate (co)variance components amidst traits using Bayesian inference with the BLUPF90 family of programs (Misztal et al., 2014). Specifically, threshold and linear models were performed using THRGIBBS1F90 and GIBBS2F90 programs, respectively. In preliminary analyses, factors that significantly afflicted each of the scores were determined using general linear models in SAS 9.4 software (SAS Constitute, 2015). Factors tested for significance (P-value ≤ 0.05) and subsequently included in the model were breed (Black Angus = 1, Carmine Angus = 2, Black–Red Angus Cross = 3), CG, number of days between calving and the appointment of teat and udder cess with linear issue (DAYS) [with β (SE) = 0.001 (0.003) and 0.009 (0.003) for teat and udder, respectively] and quadratic upshot of DAYS (DAYS2) [with β (SE) −0.0003 (0.0001) for udder scores simply], and parity grouping (defined as parity 1 = parity group A, parity ii = parity group B, parity 3 to 4 = parity grouping C, parity 5 to 7 = parity group D, and parity ≥ 8 = parity group East) (Tabular array one). Contemporary group (defined by herd, yr of measurement, and calving flavour; with 2 calving seasons from January to June and from July to December) was fitted as a random effect, whereas breed, DAYS (DAYS2 for udder), and parity group were fitted as fixed effects. As suggested past Van Tassell et al. (1998) and Varona et al. (1999), threshold models were adopted for univariate analysis of teat and udder scores (TS_ix, US_nine, TS₇, Us_vii, and TS₂, U.s._two). For comparison, linear models were also used to analyze TS_ix, Usa_nine and TS₇, United states of america₇ scoring systems. The most appropriate teat and udder score calibration (TS_nine, U.s.a.₉, TS₇, US₇, TS_two, The states_ii) and model (threshold vs. linear) were defined based on correlation between estimated breeding values (EBVs) and corrected phenotypes, Spearman's rank correlation coefficients, boilerplate EBV accuracies (r), and deviance data criteria (DIC) of univariate analysis, and subsequently used for bivariate analysis of teat and udder score with BW, WW, and YW.

Table 1.

Effects included in models to evaluate birth weight (BW), weaning weight (WW), yearling weight (YW), teat score (TS_nine, TS_vii, TS₂), and udder score (US₉, US₇, U.s.a.₂)^ane

Trait	CG2	Breed	Sex activity	MAT3	MPE4	Pgrpv	Dayshalf dozen	Days7
BW	x	ten	ten	ten	ten
WW	ten	x	x	x	ten
YW	ten	10	x	ten
Teat score (TS9, TSseven, TStwo)	ten	10				x	x
Udder score (Usaix, US7, The statesii)	x	x				x	x	ten

Linear animal models were used for univariate analyses of BW, WW, and YW. For analyses of BW, WW, and YW, CG was fitted equally a random event, whereas breed and sex were fitted as fixed effects (Table 1). Maternal additive genetic (for BW, WW, and YW) and maternal permanent environmental (for BW and WW) effects were included.

The general animal model used tin exist represented by the post-obit matrix notation:

where y is the vector of observations; β is the vector of fixed effects; a is the vector of condiment genetic furnishings; k is the vector of maternal additive genetic effects; c is the vector of maternal permanent surroundings furnishings; d is the vector of CG; due east is the vector of residual effects; and X , Z ₁, Z ₂, Z ₃, and Z _iv are incidence matrices relating β, a , grand , c , and d to y . It was causeless that Due east[ y ] = Xβ; Var( a ) = A ⊕ G ; Var( m ) = A ⊕ G _m ; Var( c ) = I ⊕ MPE , and Var( eastward ) = I ⊕ R , where A is the relationship matrix among all animals in the full-blooded data set, ⊕ is the direct Kronecker product, G is the (co)variance matrix of straight additive genetic effects, One thousand _m is the (co)variance matrix of maternal additive genetic effects, MPE is the (co)variance matrix of maternal permanent environmental effects, I is the identity matrix, and R is the (co)variance matrix of residual effects. Vectors β, a , yard , c , and d are location parameters from the provisional distribution. A uniform distribution of β was assumed a priori, reflecting no prior knowledge virtually this vector. For the (co)variance matrices of random furnishings, inverted Wishart distributions were defined as prior distributions. Thus, the distribution of y given the parameters of location and scale was assumed according to Van Tassell et al. (1998):

y | β , a , m , c , d , R ∼ M V North [ Ten β × Z 1 a × Z 2 k × Z 3 c , I N R ] .

For each assay, 700,000 iterations were generated, retaining every 50th sample. The first 200,000 iterations were discarded equally fixed burn-in. Thus, 10,000 samples were used for (co)variance and genetic parameter estimations. Data convergence was checked through graphical analysis of sampled values.

Model selection criteria were based on correlation of resulting EBV with corrected phenotypes, Spearman's rank correlation coefficient, average r, and DIC. Estimations of r were calculated using prediction error variance (PEV), as suggested by Misztal and Wiggans (1988):

Average r was calculated using EBVs for phenotyped animals merely. Statistical significance of the deviation between average r for individual analyses was determined using Student's t-tests.

Results and Give-and-take

Population

Consistent with the CAA herdbook, lxx% of cows were Black Angus, 22% were Red Angus, and eight% were Black and Red Angus crosses. Herd sizes ranged from 35 to 213 cows. And, cows scored ranged in parity from 1st to 13th.

Teat and Udder Scores

Observed teat and udder scores ranged from 1 to ix, indicating trait variation in the Canadian Angus population, and are presented in Table ii. Consequent with previous observations that producers include teat and udder structure every bit culling criteria (Arthur et al., 1992), significantly few depression (poor) teat and udder scores were observed (Fig. one). Boldt et al. (2018) showed favorable genetic correlations between cow longevity and high teat and udder scores (0.thirty ± 0.eleven, and 0.23 ± 0.11, respectively) also reflecting the frequency of lower teat and udder scores in the current study. Thus, the advantages of collapsing teat and udder scores into different scales (TS₇ US₇ and TS₂ and US₂) were tested.

Table two.

Descriptive statistics for teat scores and udder scores (TS_nine, Usa₉, TS_vii, United states of america₇, TS₂, Us₂) for n = one,735 Canadian Angus cows^ane

Teat and udder scoring calibration	Mode	Min	Max
Teat score, TSnine	7	one	nine
Udder score, USnine	seven	1	9
Teat score, TS7	5	1	7
Udder score, USvii	5	1	7
Teat score, TS2	2	1	two
Udder score, US2	2	1	ii

Distribution of teat and udder scores observed for 1,735 Canadian Angus cows using the BIF (2016) scoring guideline where the smallest teats and tightest udders were both assigned a score of 9, and large canteen-shaped teats and pendulous udders that had lost support from suspensory ligaments were both assigned a score of i.

To estimate (co)variance components and genetic parameters 700,000 cycles with stock-still fire-in periods of 200,000 cycles for both univariate and bivariate analyses were acceptable to obtain convergence, achieve low SD and a relatively narrow 95% highest posterior density interval (HPD). Posterior ways of CG variance, additive genetic variance, residual variance, and heritability obtained for teat and udder scores (TS₉, Us_ix, TS_seven, The states₇, TS_two, US₂) are shown (Table three). Posterior means of heritability for teat and udder score ranged from 0.11 ± 0.05 to 0.33 ± 0.09 and were consequent with previous estimates for these traits in populations of beef (Sapp et al., 2003; MacNeil et al., 2006; Bradford et al., 2015) and dairy (Dube et al., 2009; Eriksson et al., 2017) cows.

Table 3.

Estimates of variance components and heritability (h ²) of birth weight (BW), weaning weight (WW), yearling weight (YW) using linear models, and teat and udder scores (TS₉, Us₉, TS_seven, US₇, TS₂, US₂) using linear models and threshold models

Trait	σ2 cg ± PSDane	σii a ± PSD2	σ2 e ± PSDtwo	h ii ± PSD2
Performance traits	Linear models
BW	6.49 ± 1.20	40.06 ± 3.12	39.97 ± ane.83	0.43 ± 0.03
WW	177.99 ± 72.98	i,469.thirty ± 113.43	2,072.60 ± 78.89	0.xx ± 0.nineteen
YW	11,590.0 ± i,321.3	2,533.90 ± 350.72	six,132.xc ± 278.92	0.18 ± 0.03
Teat and udder scores4	Linear models
Teat score, TSnine	0.16 ± 0.11	0.53 ± 0.09	0.93 ± 0.08	0.32 ± 0.06
Udder score, US9	0.42 ± 0.27	0.22 ± 0.06	0.89 ± 0.06	0.15 ± 0.04
Teat score, TS7	0.15 ± 0.11	0.52 ± 0.09	0.88 ± 0.07	0.34 ± 0.05
Udder score, Usa7	0.41 ± 0.26	0.20 ± 0.05	0.85 ± 0.05	0.14 ± 0.04
Teat and udder scores	Threshold models
Teat score, TS9	0.18 ± 0.14	0.30 ± 0.14	ii.19 ± 0.25	0.11 ± 0.05
Udder score, The states9	0.38 ± 0.28	0.45 ± 0.14	2.x ± 0.28	0.fifteen ± 0.05
Teat score, TSseven	ane.39 ± 1.10	4.14 ± 0.92	7.55 ± 2.35	0.33 ± 0.09
Udder score, Us7	3.21 ± 2.19	2.45 ± 0.61	8.71 ± 1.48	0.17 ± 0.05
Teat score, TS2	0.19 ± 0.22	0.33 ± 0.33	1.00 ± 0.05	0.xviii ± 0.fourteen
Udder score, UStwo	0.33 ± 0.29	0.52 ± 0.37	1.00 ± 0.05	0.26 ± 0.12

Linear and Threshold Models

Pearson correlations betwixt corrected phenotypes and EBVs, Spearman'due south rank correlation coefficients betwixt corrected phenotypes and EBVs, average r, and estimates of DIC from analyses based on linear and threshold models are presented in Tabular array iv. Linear models outperformed threshold models (TS₉, US₉ and TS₇, Us_seven) with significantly (P < 0.05) higher corrected phenotype and EBV correlations, higher Spearman'southward rank correlations, and lower estimates of DIC. Differences in average r were significant for TS₉, Usa_ix and there was no meaning divergence for TS₇, U.s.a._seven.

Tabular array 4.

Pearson correlation of corrected phenotypes with EBVs using linear and threshold models, and TS_ix, US₉, TS_seven, The states₇ and TS_ii, US₂ scales for teat and udder score

	Linear models1				Threshold models1
Traits2	Cor (EBV, y*)	Rho	Average r	DIC	Cor (EBV, y*)	Rho	Average r	DIC
Teat score, TS9	0.88	0.85	0.64 (0.03)	5,353.77	0.64	0.65	0.45 (0.05)	6,617.77
Udder score, United states of americanine	0.72	0.71	0.52 (0.04)	v,033.45	0.54	0.57	0.49 (0.04)	5,685.75
Teat score, TSvii	0.88	0.86	0.65 (0.03)	5,261.34	0.78	0.77	0.62 (0.04)	8,084.93
Udder score, United statesvii	0.72	0.71	0.fifty (0.04)	4,985.51	0.64	0.63	0.51 (0.05)	viii,475.42
Teat score, TS2					0.78	0.44	0.21 (0.09)	4,383.76
Udder score, UStwo					0.59	0.43	0.38 (0.09)	one,442.99

Despite suggestions (Weller et al., 1988; Van Tassell et al., 1998), that threshold models are most advisable for traits of ordinal nature, nosotros inferred a linear model was more appropriate for analysis of structural traits in Canadian Angus cows. Linear models are frequently used for the evaluation of ordinal type traits in livestock species (Bradford et al., 2015; Pérez-Cabal and Charfeddine, 2015; McLaren et al., 2016; de Lacerda et al., 2019) and should exist explored when evaluating ordinal type traits, if advisable. In cases where the well-nigh advantageous phenotype is not at one terminate of the scale, such every bit feet and leg structure (Jeyaruban et al., 2012), threshold models may exist most effective.

Teat and Udder Score Scales

Pearson correlations between corrected phenotypes and EBVs, Spearman's rank correlation coefficients, boilerplate r, and estimates of DIC from analyses based on TS₉, U.s.₉, TS_vii, US₇, TS_two, US₂ are presented in Tabular array 4. Using linear models, there was no significant (P < 0.05) advantage of either scoring scale (TS₉, US₉ or TS₇, US₇). Using threshold models, TS₇, US_seven resulted in higher corrected phenotype and EBV correlations than TS_ix, United states of america₉, and higher Spearman's rank correlation and average r than TS_nine, US₉ and TS₂, US₂. Even so, estimates of DIC using TS₇, U.s._seven were too higher.

Based on the suggestion that in that location may be a threshold where teat and udder structure touch on moo-cow wellness and longevity as well as calf functioning from birth to weaning, and that a skilful verses bad structure scoring system may exist easier for producers (Persson Waller et al., 2014; Bristles et al., 2018), a binary analysis of teat and udder score (TS_two, US₂) was attempted. Using threshold models and DIC values for model evaluation, DIC values were everyman for TS₂, US₂, and highest for TS₇, US₇. In improver, correlations betwixt corrected phenotypes and EBVs for TS₂, US_ii were higher than the TS₉, Usa_nine only not different from TS₇, US_vii. Conversely, when compared to TS2, US2, average r was better for TS_vii, US₇ and TS9, US9. In improver, Spearman's rank correlation coefficients were lower for TS₂ and Usa₂ and highest for TS_seven and US_vii. When compared, using correlations, rank correlations, and average r estimates from linear models (TS₉, United states₉ and TS₇, Us₇) treating teat and udder score as a binary trait may issue in slower genetic proceeds. Furthermore, as producers make genetic proceeds for these traits observations of scores below six should decrease. Thus, necessitating a reconstruction of the binary classes, or necessitating increasing the number of classes.

Teat and Udder Correlations

Estimates of genotypic and phenotypic correlations between teat and udder score were 0.71 (0.09) and 0.46 (0.02), respectively (Table 5). These results are in agreement with previous correlations reported for Hereford and Brahman composites (Bunter and Johnston, 2014; Bradford et al., 2015). Bradford et al. (2015) observed that correlations between teat and udder scores were lower when taken past trained scorers than producer-collected data. It is possible that producers are not able to separate intrinsic factors of each trait and thus they group teat and udder as i mammary unit. Therefore, to accomplish highest rates of genetic gain, evaluation and selection of these two traits should be done independently.

Table 5.

Estimates (SD) of genetic correlations (above diagonal) and phenotypic correlations (below diagonal) for teat and udder scores (TS₉, Usa_nine) with birth weight (BW), weaning weight (WW), and yearling weight (YW)

	BW	WW	YW	Teat1, TS9	Udder2, US9
BW	1	0.35 (0.06)	0.38 (0.07)	−0.14 (0.08)	−0.04 (0.10)
WW	0.28 (0.02)	1	0.79 (0.17)	−0.09 (0.17)	−0.06 (0.18)
YW	0.22 (0.02)	0.60 (0.03)	ane	−0.14 (0.ten)	−0.twenty (0.12)
Teatone, TS9	−0.08 (0.03)	−0.08 (0.04)	−0.fourteen (0.06)	i	0.71 (0.09)
Udder2, Usanine	−0.04 (0.03)	0.01 (0.04)	−0.01 (0.06)	0.46 (0.02)	1

Performance Traits

Descriptive statistics for performance traits are shown in Tabular array 6. Posterior means of heritability for BW, WW, and YW were 0.54 ± 0.02, 0.20 ± 0.xix, and 0.xiv ± 0.02, respectively (as shown in Table iii). These values were consequent with estimates used past CAA and the American Angus Association (American Angus Association, 2017) as well as previously estimated for Angus populations (Bennett and Gregory, 1996; Espasandin et al., 2013). Linear models were used for bivariate assay of teat and udder scores (TS₉, United states₉) with BW, WW, and YW. Genetic and phenotypic correlations of teat and udder score with performance traits (BW, WW, and YW) were of low magnitude, and not dissimilar from zippo, ranging from −0.04 (0.01) to −0.20 (0.12). Had genetic and phenotypic correlations of teat and udder score with operation traits (BW, WW, and YW) been of larger magnitude and consequent with previously reported negative correlations (Sapp et al., 2004; Smith et al., 2017) then selection to better calf operation from birth to yearling could, in long term, bear upon teat and udder structure in the Angus cow herd negatively. No significant genetic correlations with production traits, primary selection goals within beef herds, emphasize the importance of scoring teat and udder structure aiming to promote progress for both performance and structure simultaneously.

Table half-dozen.

Descriptive statistics for performance traits including nascence weight (BW), weaning weight (WW), yearling weight (YW) on progeny of 1,735 Canadian Angus cows measured for teat and udder structure

Performance traits	N 1	CG,two N	Mean, kg	SD3	Min, kg	Max, kg
BW	9,916	275	37.82	10.01	17.24	58.06
WW	8,110	259	271.79	108.70	136.53	439.08
YW	4,828	187	431.09	212.82	210.92	635.03

Inference

Bovine mammary structure is complex (Franz et al., 2009) and critical to the economical value of the cattle industry (Rowson et al., 2012). In dairy, the significance and impact of teat and udder structure on milk production is well documented (Ventorp and Michanek, 1992; Dube et al., 2009). Teat and udder structure evaluations for dairy cattle are designed for milk product and automated milking efficiencies; there are numerous relevant traits, including udder attachment, udder tiptop, udder width, udder depth, udder residue, teat placement, teat bending, teat length, teat thickness, somatic cell count, and milking speed (Boettcher et al., 1998; Samoré et al., 2010; Carlström et al., 2016). In beef cattle, impacts of teat and udder structure are multifaceted, influencing both moo-cow health and longevity, also as calf performance. As cows with large, funnel-shaped teats and pendulous udders were at increased risk of mastitis (Persson Waller et al., 2014), this can direct bear upon moo-cow health and increase antibiotic use. Mastitis has also been reported to bear on calf performance due to mammary tenderness that causes cows to kicking when a dogie attempts to suckle or through reductions in milk volume and quality (Nickerson et al., 2000; Lents et al., 2002). Consequently, mastitis can result in an boilerplate nine.6 to 19 kg reduction in WW (Haggard et al., 1983; Watts et al., 1986; Newman et al., 1991). Teat and udder structure also impact calves' ability to suckle, as pendulous udders and large bottle-shaped teats are more hard to notice and suckle (Bunter and Johnston, 2014), whereas well-attached udders and moderate teats were associated with improve calf operation (Paputungan and Makarechian, 2000; Goonewardene et al., 2003). In some other report (Smith et al., 2017), calf operation was greatest from dams with moderate teat and udder scores. In the nowadays study, the youngest cows generally had the smallest teats and udders, consistent with numerically lower teat and udder scores associated with older cows observed past Smith et al. (2017). It is likely that cows that maintain moderate teat and udder scores throughout their productive life are optimal. Thus, BIF identified 2 traits, udder suspension and teat size, equally priorities for the beef industry.

Bradford et al. (2015) reported that teat and udder are only moderately repeatable with values (SE) ranging from 0.44 (0.01) to 0.47 (0.01), suggesting that producers may not be able to accurately identify, early in life, heifers that are probable to maintain efficient teat and udder structures. Early identification of optimal genetics tin bear upon profitability of beef production and significantly increase wellness and welfare of the moo-cow herd (Van Eenennaam et al., 2011; Samarajeewa et al., 2012; Ramsey et al., 2015; White et al., 2015). Estimates of heritability (SD) for teat and udder scores [0.32 (0.06) and 0.15 (0.04), respectively] for this population indicated that genetic selection can exist successful. Optimal genetic improvement for teat and udder structure within Canadian Angus cattle depends on the development of optimal genetic evaluations for the traits. Investigation of possible information handling and model selection toward an optimal genetic evaluation for teat and udder structure indicated that, for this population, teat and udder traits using TS_ix, The states_nine and linear models were nigh appropriate, and that there was no advantage to collapsing score categories or applying threshold models.

Footnotes

Financial support received from Agriculture and Agri-Food Canada, the Alberta Livestock and Meat Agency (now Alberta Agriculture and Forestry), and the Canadian Angus Clan (CAA). This written report would not take been possible without cooperation of Canadian Angus producers and genetic evaluation expertise from the Canadian Beefiness Breeds Council, Calgary, Alberta. Many cheers to Drs John Kastelic and Graham Plastow for their back up.

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