Economic Aspects of Animal Breeding in Switzerland - Qualitas AG

About the job

Simon Schlebusch examined a bioeconomic approach as part of his master's thesis to calculate the economic weights of individual traits in the breeding goals of Brown Swiss and Holstein cattle.

Background

The question of the optimal breeding goal for dairy cows is as old as breeding itself. Previously (before 1970), the selection of breeding animals was mainly determined by the external appearance of the animals. This changed with increasing division of labour, the associated specialisation of farms, and the growing economic importance of production traits such as milk yield, fat content, and protein content. As a consequence, these traits were assigned a dominant role in the breeding goal.

Due to this development, scientifically based statistical methods such as the BLUP animal model were developed to estimate breeding values. The availability of cheap computing power enabled the introduction of these methods in breeding organisations. This technical development, together with the introduction of reproductive technologies, led to a significant increase in milk production performance.

BLUP Animal Model

BLUP stands for Best Linear Unbiased Prediction and refers to a class of statistical estimation methods with defined properties. This method is used to estimate unknown quantities, such as breeding values.

Breeding Goals and the Overall Breeding Value

The liberalisation of the milk market, i.e. the removal of price guarantees and milk quotas, led to a sharp drop in milk prices. For breeding, this meant that other traits such as longevity, somatic cell counts, fertility, and various health traits gained greater economic importance.

With the expansion of the breeding goal to include additional traits, the question arose as to how these characteristics could be considered in the selection of breeding animals. The answer to this question was provided by the overall breeding value. Ideally, selection decisions are made based on a criterion that allows a clear ranking of selection candidates.

The optimal criterion for selecting livestock based on multiple traits is the overall breeding value, as it corresponds to the mathematical formulation of the breeding goal and is calculated as a weighted average of the breeding values. The economic weights are used as weighting factors. The economic weight of a trait corresponds to the change in profit of a production herd with a marginal change in the population mean of the respective trait.

An alternative way to weight traits in an overall breeding value is to specify the desired selection gains for individual traits and determine the weighting factors of the individual traits based on these specifications. This method of determining weighting factors is called 'Desired Gains'.

This method is very flexible and the breeding goal can easily be adapted to any wishes of breeders. However, it should be noted that the weighting factors derived in this way do not take into account the economic context of individual farms. In a time of declining returns, the pressure to economically optimise production increases, as otherwise no profits can be made and thus the existence of farms is called into question.

Bioeconomic Model

The relationships described in the previous sections demonstrate the importance of economic weights. These can be calculated using a bioeconomic model. For this purpose, a herd model is created in which herd size, milk yield, fertility, etc. are defined. The graphic below shows the various influences in the herd model.

The herd structure used in the model was based on the following parameters:

• Herd size set at 50 cows with defined values for average milk quantity
• Protein and fat content
• Somatic cell counts.

The modelled production farm did not rear its own replacement heifers for culled cows. All removals were replaced by purchased pregnant heifers. Accordingly, all cows were inseminated with SILIAN and the calves were sold as feeders after four weeks.

In the second part of the bioeconomic model, the profit equation is defined. The profit or loss of the modelled production herd is calculated. Income consists of milk money, sales of culled cows, sales of feeders, and direct payments.

Expenses include feed costs, cattle purchases, insemination costs, as well as fixed and veterinary costs. Milk money consists of base payments and payments for protein and fat content, as well as bonus payments for somatic cell counts expressed by the Somatic Cell Score (SCS).

The profit achieved in the model is only of limited interest for calculating economic weights, as economic weights correspond to the change in profit caused by a change in input. The input is only slightly changed so that all assumptions in the model still apply.

Case Study for the Brown Swiss Breed

The following table shows this expressed in numbers for the trait milk yield in the Brown Swiss breed, but the concept can also be applied to other traits and breeds.

The multiplication of the economic weight and the genetic standard deviation, which for Brown Swiss for the trait milk is 564.78 kg, results in 0.1001 * 564.78 = 56.55 CHF/genetic standard deviation as the economic weight.

The results of the economic weight per genetic standard deviation for the Brown Swiss breed are shown in the next table. The results show that the trait longevity has the highest economic weight per genetic standard deviation.

The graphic shows that the economic weights determine the weighting factors of the breeding values of the individual traits in the overall breeding value.

It shows that functional traits make up 90% of the index and the milk complex about 10%. It also shows the importance of the trait longevity in relation to economic efficiency in breeding. Also important are SCS with 30% and fertility with 20% share of the overall breeding value.

The results shown here illustrate that embedding breeding and the overall breeding value in an economic context is very important both for the individual farm and for the entire breed in order to remain attractive and economically sound in the future. The implementation of the fundamentals developed here into practical breeding work is therefore very important.

Milk yield: 2%

Protein: 4%

Fat: 4%

NRR: 19%

Longevity: 41%

SCS: 30%

Master's Thesis at Qualitas AG

The master's thesis was created in collaboration between Simon Schlebusch and Qualitas. During his internship, the master's graduate gained insight into dairy cattle breeding in Switzerland and was directly supervised by Peter von Rohr at Qualitas.

Simon will continue to focus on the topic of economic efficiency in animal breeding. This time with a dissertation aimed at calculating the economic value of a cow within the herd and thus making culling decisions at the farm level economically transparent.

At Qualitas, we regularly offer topics for master's theses and internships in breeding value estimation. If you are interested, please contact us and read about what an internship at Qualitas looks like.