Analyzing the heterogeneity of farmers’ preferences for improvements in dairy cow traits using farmer typologies

AbstractGiving consideration to farmers’ preferences for improvements in animal traits when designing genetic selection tools such as selection indexes might increase the uptake of these tools. The increase in use of genetic selection tools will, in turn, assist in the realization of genetic gain in breeding programs. However, the determination of farmers’ preferences is not trivial because of its large heterogeneity. The aim of this study was to quantify Australian dairy farmers’ preferences for cow trait improvements to inform and ultimately direct the choice of traits and selection indexes in the 2014 review of the National Breeding Objective. A specific aim was to analyze the heterogeneity of preferences for cow trait improvements by determining whether there are farmer types that can be identified with specific patterns of preferences. We analyzed whether farmer types differed in farming system, socioeconomic profile, and attitudes toward breeding and genetic evaluation tools. An online survey was developed to explore farmers’ preferences for improvement in 13 cow traits. The pairwise comparisons method was used to derive a ranking of the traits for each respondent. A total of 551 farmers fully completed the survey. A principal component analysis followed by a Ward hierarchical cluster analysis was used to group farmers according to their preferences. Three types of farmers were determined: (1) production-focused farmers, who gave the highest preference of all for improvements in protein yield, lactation persistency, feed efficiency, cow live weight, and milking speed; (2) functionality-focused farmers with the highest preferences of all for improvements in mastitis, lameness, and calving difficulty; and (3) type-focused farmers with the highest preferences of all for mammary system and type. Farmer types differed in their age, their attitudes toward genetic selection, and in the selection criteria they use. Surprisingly, farmer types did not differ for herd size, calving, feeding system, or breed. These results support the idea that preferences for cow trait improvements are intrinsic to farmers and not to production systems or breeds. As a result of this study, and some bioeconomic modeling (not included in this study), the Australian dairy industry has implemented a main index and 2 alternative indexes targeting the different farmer types described here

A breeding index to rank beef bulls for use on dairy females to maximize profit

peer-reviewedThe desire to increase profit on dairy farms necessitates consideration of the revenue attainable from the sale of surplus calves for meat production. However, the generation of calves that are expected to excel in efficiency of growth and carcass merit must not be achieved to the detriment of the dairy female and her ability to calve and re-establish pregnancy early postcalving without any compromise in milk production. Given the relatively high heritability of many traits associated with calving performance and carcass merit, and the tendency for many of these traits to be moderately to strongly antagonistic, a breeding index that encompasses both calving performance and meat production could be a useful tool to fill the void in supporting decisions on bull selection. The objective of the present study was to derive a dairy–beef index (DBI) framework to rank beef bulls for use on dairy females with the aim of striking a balance between the efficiency of valuable meat growth in the calf and the subsequent performance of the dam. Traits considered for inclusion in this DBI were (1) direct calving difficulty; (2) direct gestation length; (3) calf mortality; (4) feed intake; (5) carcass merit reflected by carcass weight, conformation, and fat and the ability to achieve minimum standards for each; (6) docility; and (7) whether the calf was polled. Each trait was weighted by its respective economic weight, most of which were derived from the analyses of available phenotypic data, supplemented with some assumptions on costs and prices. The genetic merit for a range of performance metrics of 3,835 artificial insemination beef bulls from 14 breeds ranked on this proposed DBI was compared with an index comprising only direct calving difficulty and gestation length (the 2 generally most important characteristics of dairy farmers when selecting beef bulls). Within the Angus breed (i.e., the beef breed most commonly used on dairy females), the correlation between the DBI and the index of genetic merit for direct calving difficulty plus gestation length was 0.74; the mean of the within-breed correlations across all other breeds was 0.87. The ranking of breeds changed considerably when ranked based on the top 20 artificial insemination bulls excelling in the DBI versus excelling in the index of calving difficulty and gestation length. Dairy breeds ranked highest on the index of calving difficulty and gestation length, whereas the Holstein and Friesian breeds were intermediate on the DBI; the Jersey breed was one of the poorest breeds on DBI, superior only to the Charolais breed. The results clearly demonstrate that superior carcass and growth performance can be achieved with the appropriate selection of beef bulls for use on dairy females with only a very modest increase in collateral effect on cow performance (i.e., 2–3% greater dystocia expected and a 6-d-longer gestation length)

Heterogeneity of genetic parameters for calving difficulty in Holstein heifers in Ireland

Calving difficulty is a trait that greatly affects animal welfare, herd profitability, and the amount of labor required by cattle farmers. It is influenced by direct and maternal genetic components. Selection and breeding strategies can optimize the accuracy of genetic evaluations and correctly emphasize calving difficulty in multiple-trait indices provided there are accurate estimates of genetic parameters. In Ireland, large differences exist in the age at which heifers first give birth to calves. The objective of this study was to estimate genetic parameters for calving difficulty in first-parity Holsteins and to determine whether these differed with age of the heifer at calving. Transformed calving difficulty records for 18,798 Holstein heifers, which calved between January 2002 and May 2006, were analyzed using univariate, multitrait, and random regression linear sire-maternal grandsire models. The model that 1) fitted a second-order random regression of dam age at first parity for the direct component, 2) treated the maternal component as a single trait regardless of dam age, and 3) fitted a single residual variance component was optimal. Heritabilities for direct (0.13) and maternal (0.04) calving difficulty were significantly different from zero. These 2 components were moderately negatively correlated (¿0.47). Estimates of direct genetic variance and heritability were heterogeneous along the dam age trajectory, decreasing initially with dam age before subsequently increasing. Heritability estimates ranged between 0.11 and 0.37 and were higher for records with younger and older dams at parturition. Genetic correlations between the direct components of calving difficulty decreased from unity to 0.5 with increasing distance between dam ages at parturition. The results of this study indicated that heterogeneity of direct genetic variance existed for calving difficulty, depending on dam age at first parturition

Genetic relationships between carcass cut weights predicted from video image analysis and other performance traits in cattle

peer-reviewedThe objective of this study was to quantify the genetic associations between a range of carcass-related traits including wholesale cut weights predicted from video image analysis (VIA) technology, and a range of pre-slaughter performance traits in commercial Irish cattle. Predicted carcass cut weights comprised of cut weights based on retail value: lower value cuts (LVC), medium value cuts (MVC), high value cuts (HVC) and very high value cuts (VHVC), as well as total meat, fat and bone weights. Four main sources of data were used in the genetic analyses: price data of live animals collected from livestock auctions, live-weight data and linear type collected from both commercial and pedigree farms as well as from livestock auctions and weanling quality recorded on-farm. Heritability of carcass cut weights ranged from 0.21 to 0.39. Genetic correlations between the cut traits and the other performance traits were estimated using a series of bivariate sire linear mixed models where carcass cut weights were phenotypically adjusted to a constant carcass weight. Strongest positive genetic correlations were obtained between predicted carcass cut weights and carcass value (min rg(MVC)50.35; max rg(VHVC)50.69), and animal price at both weaning (min rg(MVC)50.37; max rg(VHVC)50.66) and post weaning (min rg(MVC)50.50; max rg(VHVC)50.67). Moderate genetic correlations were obtained between carcass cut weights and calf price (min rg(HVC)50.34; max rg(LVC)50.45), weanling quality (min rg(MVC)50.12; max rg(VHVC)50.49), linear scores for muscularity at both weaning (hindquarter development: min rg(MVC)520.06; max rg(VHVC)50.46), post weaning (hindquarter development: min rg(MVC)50.23; max rg(VHVC)50.44). The genetic correlations between total meat weight were consistent with those observed with the predicted wholesale cut weights. Total fat and total bone weights were generally negatively correlated with carcass value, auction prices and weanling quality. Total bone weight was, however, positively correlated with skeletal scores at weaning and post weaning. These results indicate that some traits collected early in life are moderate-to-strongly correlated with carcass cut weights predicted from VIA technology. This information can be used to improve the accuracy of selection for carcass cut weights in national genetic evaluations

Genetic variation in wholesale carcass cuts predicted from digital images in cattle

peer-reviewedThe objective of this study was to quantify the genetic variation in carcass cuts predicted using digital image analysis in commercial cross-bred cattle. The data set comprised 38 404 steers and 14 318 heifers from commercial Irish herds. The traits investigated included the weights of lower value cuts (LVC), medium value cuts (MVC), high value cuts (HVC), very high value cuts (VHVC) and total meat weight. In addition, the weights of total fat and total bones were available on the steers. Heritability of carcass cut weights, within gender, was estimated using an animal linear model, whereas genetic and phenotypic correlations among cuts were estimated using a sire linear model. Carcass weight was included as a covariate in all models. In the steers, heritability ranged from 0.13 (s.e.50.02) for VHVC to 0.49 (s.e.50.03) for total bone weight, and in the heifers heritability ranged from 0.15 (s.e.50.04) for MVC to 0.72 (s.e.50.06) for total meat weight. The coefficient of genetic variation for the different cuts varied from 1.4% to 3.6%. Genetic correlations between the different cut weights were all positive and ranged from 0.45 (s.e.50.08) to 0.89 (s.e.50.03) in the steers, and from 0.47 (s.e.50.14) to 0.82 (s.e.50.06) in the heifers. Genetic correlations between the wholesale cut weights and carcass conformation ranged from 0.32 (s.e.50.06) to 0.45 (s.e.50.07) in the steers, and from 0.10 (s.e.50.12) to 0.38 (s.e.50.09) in the heifers. Genetic correlations between the same wholesale cut traits in steers and heifers ranged from 0.54 (s.e.50.14) for MVC to 0.79 (s.e.50.06) for total meat weight; genetic correlations between carcass weight and carcass classification for conformation and fat score in both genders varied from 0.80 to 0.87. The existence of genetic variation in carcass cut traits, coupled with the routine availability of predicted cut weights from digital image analysis, clearly shows the potential to genetically improve carcass value