Developing breeding objectives for dairy cattle in Russia
[Contact: Prof. Hassan Baneh,]

The main objective of any breeding program is to improve the profitability of the production system, which will be affected by several factors and economic traits. The traits can contribute to profitability by increasing income or by decreasing costs. Therefore, in order to maximize the economic progress, the selection candidates must be evaluated for several traits via a "multiple-trait selection index" . The index, which incorporates economic values for individual traits, is the most efficient way to maximize genetic improvement in overall breeding objectives. Economic values estimates are strongly dependent to the production condition and product pricing system. The economic values for production and functional traits on dairy cattle have been estimated and applied to construct country-specific national genetic indexes.

Russia has over 8 million dairy cattle, playing a critical role in the country's agricultural sector. The dairy cattle production system in Russia differs from other countries not only in terms of climate conditions and management systems but also in the milk pricing system, which is mainly based on milk yield. Currently, the economic-breeding indexes developed for other countries are applied in breeding programs, and the Russian dairy cattle sector is suffering from a lack of "national breeding objectives for dairy cattle." Therefore, this project is aimed to:

  1. Identify key factors and traits of economic importance for the Russian dairy industry.
  2. Estimate the economic value for production, reproduction, and functional traits under different production systems.
  3. Develop breeding objectives that considers the unique needs and priorities of the Russian dairy sector.
  4. Construct a "national economic selection index" for Russian dairy industry.

Required skills: Knowledge of dairy cattle industry, quantitative genetics, animal breeding programs (genetic evaluation and selection index theory). The candidate should be proficient in statistical analysis for large data set and have experience with quantitative genetics analysis and bio-economic models. Programming skills and experience are considered advantageous.