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A REVIEW OF THE NATIONAL BREEDING OBJECTIVE AND SELECTION INDEXES FOR THE AUSTRALIAN DAIRY INDUSTRY

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We applied a pairwise comparison method using the 1000Minds® software to assess farmers' preferences for cow trait improvements. A Principal Component Analysis (PCA) followed by a Cluster Analysis (CA) of the principal components led to the identification of three farmer clusters (farmer types in the rest of this document) according to the trait improvements to which the farmers had the highest preference. This way, Australian dairy farmers can be classified into production-focused (n = 192), functionality-focused (n = 187), and type-focused (n = 172) farmers. As a result of this study, and bio-economic modelling, three indexes were released to the Australian dairy industry. The Balanced Performance Index aligns with the average preferences, while the Health Weighted and Type Weighted indexes reflect the preferences identified for functionally-focused and type-focused farmer types, respectively. These three indexes include new traits and offer a range of options to choose from when selecting bulls, while all driving gain towards the National Breeding Objective (NBO). INTRODUCTION Breeding objectives can play an important, but not exclusive, role in determining the optimal size and direction of genetic changes in traits. Economically efficient multiple-trait selection is normally achieved through the definition of breeding objectives and the development of appropriate selection indexes for specific production systems (James 1981). In nations with industrialised dairy industries a breeding objective is often controlled at the national level (e.g. Harris et al. 1996). The NBO underpins the selection index for the ranking of dairy cattle for profitable genetic merit in Australia (Pryce et al. 2010). The aim of this study was to update the NBO by calculating economic weights for a range of traits that impact profitability of Australian dairy farms. The final choice of selection indexes was informed by analysing the heterogeneity of farmers' preferences (from surveys) for improvements in dairy cow traits using farmer typologies. This paper broadly describes the methodology used to analyse heterogeneity of farmers' preferences and how the outcomes of this were used, along with economic analysis underpinning the breeding objective, to develop selection indexes.
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A REVIEW OF THE NATIONAL BREEDING OBJECTIVE AND SELECTION INDEXES
FOR THE AUSTRALIAN DAIRY INDUSTRY
T. Byrne1, D. Martin-Collado1, B. Santos1,2, P. Amer1, J. Pryce3, and M. Axford4
1AbacusBio Limited, Dunedin 9016, New Zealand
2School of Environmental & Rural Science, University of New England, Armidale, Australia
3Department of Economic Development, Jobs, Transport and Resources and La Trobe University,
Bundoora, VIC 3083, Australia
4Australian Dairy Herd Improvement Scheme, Melbourne VIC 3000, Australia
SUMMARY
We applied a pairwise comparison method using the 1000Minds® software to assess farmers’
preferences for cow trait improvements. A Principal Component Analysis (PCA) followed by a
Cluster Analysis (CA) of the principal components led to the identification of three farmer clusters
(farmer types in the rest of this document) according to the trait improvements to which the farmers
had the highest preference. This way, Australian dairy farmers can be classified into production-
focused (n = 192), functionality-focused (n = 187), and type-focused (n = 172) farmers. As a result
of this study, and bio-economic modelling, three indexes were released to the Australian dairy
industry. The Balanced Performance Index aligns with the average preferences, while the Health
Weighted and Type Weighted indexes reflect the preferences identified for functionally-focused and
type-focused farmer types, respectively. These three indexes include new traits and offer a range of
options to choose from when selecting bulls, while all driving gain towards the National Breeding
Objective (NBO).
INTRODUCTION
Breeding objectives can play an important, but not exclusive, role in determining the optimal
size and direction of genetic changes in traits. Economically efficient multiple-trait selection is
normally achieved through the definition of breeding objectives and the development of appropriate
selection indexes for specific production systems (James 1981). In nations with industrialised dairy
industries a breeding objective is often controlled at the national level (e.g. Harris et al. 1996). The
NBO underpins the selection index for the ranking of dairy cattle for profitable genetic merit in
Australia (Pryce et al. 2010). The aim of this study was to update the NBO by calculating economic
weights for a range of traits that impact profitability of Australian dairy farms. The final choice of
selection indexes was informed by analysing the heterogeneity of farmers’ preferences (from
surveys) for improvements in dairy cow traits using farmer typologies.
This paper broadly describes the methodology used to analyse heterogeneity of farmers’
preferences and how the outcomes of this were used, along with economic analysis underpinning
the breeding objective, to develop selection indexes.
METHODS
Survey questionnaire and analysis. We applied a pairwise comparison method to assess farmers’
preferences for trait improvements, using the 1000Minds® software. This software is simple to
implement and reduces the level of burden on respondents compared to other more complex methods
(Hansen and Ombler 2009). The software asks a series of questions to respondents, who are asked
to choose, repeatedly, between pairs of alternatives until all possible pairs of alternatives are
evaluated. A ranking of the presented alternatives is derived from these choices. We considered most
of the traits included in the Australian Profit Ranking (APR), at the time of surveying, as well as
other traits that were considered of potential importance for the Australian dairy industry. Survey
traits included; protein yield, cow live weight, fertility, longevity, mastitis resistance, milking speed,
temperament, calving difficulty, feed efficiency, lactation persistency, lameness, mammary system,
and overall type. The magnitude of the suggested improvement in each trait was such that our
estimate of the economic impact on farm would be as similar as possible across traits (Martin-
Collado et al. 2015). Farmer attitudes towards genetic evaluation tools were assessed by asking
farmers to rate, in a five-level Likert scale (Likert 1932), their level of agreement with specific
statements. Farmers were also asked a set of farmer and farm descriptors that were thought to have
a potential influence on farmers’ preferences for improvements in traits. These included farmer age,
role on farm, farm location, herd size, total milk production, cow breed distribution, cows registered
with breed society, replacements sired by AI or herd bulls, labour profile, calving system, and
feeding system. Farmers of all 6314 Australian dairy farms were sent the survey. In addition, 200
levy-paying farmers were randomly selected from the list of all Dairy Australia farmers. The survey
produced 618 responses, of which 551 were fully completed and were used for this study.
A Principal Component Analysis (PCA) followed by a Cluster Analysis (CA) of the principal
components was used to investigate the patterns of relationships between farmers’ preferences for
the different trait improvements. We determined the principal components (PCs) of the trait
preferences and implemented a Ward’s Hierarchical CA of the first five principal components. The
selection of the number of clusters was based on the loss of inertia (within cluster sum of squares)
at each partitioning of clusters (Ward 1963). We described the farmer types according to their
preferences for animal trait improvements. We analysed the relationship between farmer types and
farmer attitudes, criteria used for selecting bulls (results not shown) and other farm and farmer
descriptors (as reported above). Differences for the normally distributed variables were analysed
with the ANOVA test followed by Duncan’s multiple comparisons test to analyse pairwise
differences. The non-normally distributed variables were analysed with the Kruskal-Wallis test and
multiple comparisons were tested with the Wilcoxon’s procedure. Finally, the Fisher’s exact test
was used to analyse pairwise differences between discrete variables among farmer types.
Formulation of breeding objectives and selection indexes. Economic weights in the breeding
objective were calculated as the economic effect on profit per unit change in each of the traits
independently, allowing for the Australian dairy production system diversity of feeding systems and
calving patterns. These economic weights are reported elsewhere (Byrne et al. in preparation).
Selection indexes were defined using a combination of economic principles and desired gains
approaches, such that indexes remained relevant for improving on-farm profit based on strong
scientific principles which were also consistent with farmers’ preferences.
RESULTS AND DISCUSSION
In the overall ranking of preferences for trait improvements at population level we could
distinguish the most preferred and the least preferred trait improvements, as well as a large number
of trait improvements with medium preference. Mastitis (average rank 4.3) was the most preferred
trait followed by longevity (5.1) and fertility (5.4) whereas the least preferred traits were milking
speed (8.2), lactation persistency (8.3), and cow live weight (10.4). These preferences are relative to
crude calculations that equalise the economic effects of each offered trait difference; thus the
preferences are more likely to be driven by perception than by economics.
Principal Component Analysis of Farmers’ Preferences for Trait Improvements. The scores of
farmers’ preferences for trait improvements in the first two PCs are described in Figure 1. These
first two PC accounted for 26.6% of the total variability of the farmers’ trait improve ment
preferences, and five PCs were needed to explain 55.5% of the initial variability.
Figure 1. Scores of the preferences for improvements on cow traits on the first two principal components.
Cluster Analysis of the principal component. While the data indicates a continuum of preference,
the cluster analysis of the first five PCs determined the existence of three farmer types of very similar
sizes, named according to the trait improvements to which the farmers had the highest preference.
This way, Australian dairy farmers can be classified into production-focused (n = 192),
functionality-focused (n = 187), and type-focused (n = 172) farmers.
Production-focused farmers gave the highest preference to improving longevity (mean rank ±SE:
4.4±0.23), feed efficiency (5.2±0.22), and protein yield (5.3±0.23). Compared to the other farmer
types production-focused farmers gave the highest importance of all to protein yield, lactation
persistency (6.3±0.25), feed efficiency, cow live weight (9.0±0.25), and milking speed (6.9±0.26).
Conversely, they gave lowest importance of all the farmer types to improving mastitis (5.8±0.27),
lameness (8.1±0.23), and mammary system (8.4±0.21).
Functionality-focused farmers gave the highest preference to mastitis (2.8±0.17), followed by
lameness (4.6±0.26), calving difficulty (5.2±0.22), and fertility (5.4±0.25). Compared to the other
farmer types, functionality-focused farmers gave the highest preference of all to mastitis, lameness,
and calving difficulty.
Type-focused farmers preferred improvements in mammary system (3.7±0.15), longevity
(4.0±0.19) and mastitis (4.1±0.20) the most. Compared to the other farmer types, type-focused
farmers gave the highest preference of all to mammary system, and type (4.9±0.19). On the contrary,
type-focused farmers gave the lowest importance of all to protein yield (8.5±0.22).
There was an expectation that factors such as farm size and calving or feeding system would
explain some of the variability in farmers’ preferences for trait improvements, but we did not find
significant differences between farmer types for any of the farm descriptors. However, in a
univariate analysis of the survey results, we observed that the importance given to specific traits was
related to some of the farm features. Seasonal calving farmers gave higher preference (ANOVA p-
value < 0.05), average rank 4.9, to an improvement in cow fertility compared to farmers of split-
calving herds (5.5) and all-year-round herds (5.8) and to not increasing live weight (ANOVA p-
value < 0.001), average rank 9.6, compared to the other calving systems (pooled average of 10.7).
There was also no clear relationship between farmers’ preferences and breed when analysing the PC
clusters. The results could imply that farmers’ preferences are intrinsic to the farmer, rather than
being strongly linked to external system factors.
Formulation of selection indexes informed by farmers preferences. Australian dairy farmers
can be divided into three types according to the pattern of their preferences for trait improvements.
As a result of detailed bio-economic modelling, and this study, three indexes were released to
Australian dairy farmers (Figure 2) in September 2014. These three indexes include new traits,
informed by trait preference data, and offer a range of options to choose from when selecting bulls.
The Balanced Performance Index aligns with the average preferences, while the Health Weighted
and Type Weighted Indexes reflect the preferences identified for Functionally-focused and Type-
focused farmer types, respectively. The economic weights for all traits were calculated based on
economic principles, with the exception of a number of trait weightings in the Type-weighted index,
which were calculated using a desired gains approach informed by trait preference data.
Figure 2. Relative emphasis in the three new indexes and the APR.
CONCLUSION
There are different groups of Australian dairy farmers with specific needs. This has led to the
three indexes including new traits and offers a range of options when selecting bulls.
REFERENCES
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... The collective efforts of breeding schemes worldwide have slowed down the negative trends in fertility around the world, and some countries have even reported a positive trend in some female fertility traits (). Additional efforts like those described here to increase the reliability of fertility EBV and those described in Byrne et al. (2015) in developing indexes with greater emphasis on fertility may ensure that future genetic trends in female fertility in dairy cattle are favorable. ...
Article
The objectives of this study were (1) to propose changing the selection criteria trait for evaluating fertility in Australia from calving interval to conception rate at d 42 after the beginning of the mating season and (2) to use type traits as early fertility predictors, to increase the reliability of estimated breeding values for fertility. The breeding goal in Australia is conception within 6 wk of the start of the mating season. Currently, the Australian model to predict fertility breeding values (expressed as a linear transformation of calving interval) is a multitrait model that includes calving interval (CVI), lactation length (LL), calving to first service (CFS), first nonreturn rate (FNRR), and conception rate. However, CVI has a lower genetic correlation with the breeding goal (conception within 6 wk of the start of the mating season) than conception rate. Milk yield, type, and fertility data from 164,318 cow sired by 4,766 bulls were used. Principal component analysis and genetic correlation estimates between type and fertility traits were used to select type traits that could subsequently be used in a multitrait analysis. Angularity, foot angle, and pin set were chosen as type traits to include in an index with the traits that are included in the multitrait fertility model: CVI, LL, CFS, FNRR, and conception rate at d 42 (CR42). An index with these 8 traits is expected to achieve an average bull first proof reliability of 0.60 on the breeding objective (conception within 6 wk of the start of the mating season) compared with reliabilities of 0.39 and 0.45 for CR42 only or the current 5-trait Australian model. Subsequently, we used the first eigenvector of a principal component analysis with udder texture, bone quality, angularity, and body condition score to calculate an energy status indicator trait. The inclusion of the energy status indicator trait composite in a multitrait index with CVI, LL, CFS, FNRR, and CR42 achieved a 12-point increase in fertility breeding value reliability (i.e., increased by 30%; up to 0.72 points of reliability), whereas a lower increase in reliability (4 points, i.e., increased by 10%) was obtained by including angularity, foot angle, and pin set in the index. In situations when a limited number of daughters have been phenotyped for CR42, including type data for sires increased reliabilities compared with when type data were omitted. However, sires with more than 80 daughters with CR42 records achieved reliability estimates close to 80% on average, and there did not appear to be a benefit from having daughters with type records. The cost of phenotyping to obtain such reliabilities (assuming a cost of AU$14 per cow with type data and AU$5 per cow with pregnancy diagnosed) is lower if more pregnancy data are collected in preference to type data. That is, efforts to increase the reliability of fertility EBV are most cost effective when directed at obtaining a larger number of pregnancy tests.
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