This study comprises an update of the economic values for dairy traits for the Australian industry and the formulation of updated selection indices. An economic model, which calculates partial economic values for each trait individually, was developed to determine the economic implications of selective dairy breeding, based on the effect of trait changes on the profit of commercial dairy farms in Australia. Selection indices were developed from economic values, which were transformed into base economic weights by including the discounted genetic expressions coefficients. Economic weights (in Australian dollars) were 1.79, 6.92, -0.10, -5.44, 8.84, 7.68, 1.07, 4.86, 1.91, 3.51, 4.90, 0.31, 2.03, 2.00, and 0.59, for milk fat (kg), milk protein (kg), milk volume (L), body weight (kg), survival (%), residual survival (%), somatic cell count (cells/mL), fertility (%), mammary system [Australian Breeding Value (ABV) unit], temperament (ABV unit), milking speed (ABV unit), udder depth (%), overall type (%), fore udder attachment (%), and pin set (%), respectively. The updated economic weights presented in this study constituted the basis of the definition for 3 new indices. These indices were developed from combination of bioeconomic principles, patterns of farmer preferences for trait improvements, and desired gains approaches. The 3 indices, Balanced Performance Index, Health Weighted Index, and Type Weighted Index, have been released to the industry.

Giving 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.

Genomic selection has led to opportunities for developing new breeding values that rely on phenotypes in dedicated reference populations of genotyped cows. In Australia, it has been applied to 2 novel traits: feed efficiency, which was released in 2015 as feed saved breeding values, and heat tolerance genomic breeding values, released for the first time in 2017. Feed saved is already included in the national breeding objective, which is focused on profitability and designed to be in line with farmer preferences. Our future focus is on traits associated with animal health, either directly or in combination with predictor traits, such as mid-infrared spectral data and, into the future, automated data capture. Although it is common for many evaluated traits to have genomic reliabilities ranging between 60 and 75%, many new, genomic information-only traits are likely to have reliabilities of less than 50%. Pooling of phenotype data internationally and investing in maintenance of reference populations is one option to increase the reliability of these traits; the other is to apply improved genomic prediction methods. For example, advances in the use of sequence data, in addition to gene expression studies, can lead to improved persistence of genomic breeding values across breeds and generations and potentially lead to greater reliabilities. Lower genomic reliabilities of novel traits could reduce the overall index reliability. However, provided these traits contribute to the overall breeding objective (e.g., profit), they are worth including. Bull selection tools and personalized genetic trends are already available, but increased access to economic and automatic capture farm data may see even better use of data to improve farm management and selection decisions.

Mid-infrared spectroscopy (MIRS) is traditionally used for analysing milk fat, protein and lactose concentrations in dairy production, but there is growing interest in using it to predict difficult, or expensive-to-measure, phenotypes on a large scale. The resulting prediction equations can be applied to MIRS data from commercial herd-testing, to facilitate management and feeding decisions, or for genomic selection purposes. We investigated the ability of MIRS of milk samples to predict milk fatty acids (FAs) and energy balance (EB) of dairy cows in Australia. Data from 240 Holstein lactating cows that were part of two 32-day experiments, were used. Milk FAs were measured twice during the experimental period. Prediction models were developed using partial least-square regression with a 10-fold cross-validation. Measures of prediction accuracy included the coefficient of determination (R2cv) and root mean-square error. Milk FAs with a chain length of ≤16 were accurately predicted (0.89 ≤ R2cv ≤ 0.95), while prediction accuracy for FAs with a chain length of ≥17 was slightly lower (0.72 ≤ R2cv ≤ 0.82). The accuracy of the model prediction was moderate for EB, with the value of R2cv of 0.48. In conclusion, the ability of MIRS to predict milk FAs was high, while EB was moderately predicted. A larger dataset is needed to improve the accuracy and the robustness of the prediction models.

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