CURRICULUM VITAE

Objective:

To participate in a joint research project in the field of dairy sheep genetic evaluations.

Permanent position:

Associate professor -  Bioinformatics and Population Genetics

Professional experience:

- Genetic evaluations in dairy sheep, cattle and buffaloes - genetic parameters and breeding values, formulation/construction of breeding criteria.

- Study of factors influencing the production traits, methods of their accounting.

- Construction of selection systems for obtaining a better genetic gain.

Education:

Ph.D. - Agricultural Academy, Sofia, Bulgaria. 1979

Major:  Sheep Breeding

Dissertation: Comparison of methods for predicting breeding values of rams for milk yield.

M.S. Agricultural Academy, Sofia, Bulgaria. 1975

Major: Animal Genetics.

Dissertation: Effect of inbreeding level on heritability and genetic correlations in silkworm (Bombix mori).

Agricultural Academy student (zootechnics) 1970 – 1974

5-th Sofia secondary scientific school. 1966 - 1968

Research activity:

Research interests: Genetic analyses of animal breeding data, studying new traits in dairy sheep, constructing of breeding plans, building FORTRAN programs for analyses of variance components, BLUP breeding values and selection indices, fifteen years experience in PC computer programming. Modeling of genetic gain in different systems of selection and methods for breeding values estimation. Systems of selection in a big number small sized herds. Implementation of the understanding for genetic values in the herd breeding practice. Implementation of extension service in animal husbandry.

Research activity: International scientific meeting and conferences attended - 15; research support grants - 3; scientific papers in the field of sheep, cattle and buffalo breeding, sheep physiology and reproduction - 50 (see appendix).

Computer skills: statistics, computer programming (FORTRAN).

Teaching experience:

Advisor of four Master of science post graduate students in sheep breeding - 1985 - 1990

Other experience:

- FAO International Project for Red Cattle Breeds comparison in Bulgaria. 1982 - 1984

- Standardizing methods for ram genetic evaluation in Eastern Europe, coordinator. 1988 - 1989

- FAO project “Strengthening Agricultural Policy-making and Economic Services Capability” in Bulgaria. TCP/BUL/ 2352(A). National consultant. 1994 - 1995

- Experience in working in international teams.

Study, research trips and employment abroad:

- Agricultural University of Norway. “Systems of selection in dairy cattle”. 1981.

- Lincoln University - Missouri, USA. ”Extension Service Activity in Cattle and Sheep Farming, Breeding and Marketing”. 1991

- University of Nebraska - Lincoln, USA. Employed as a Post Doc in the project: “Sire by Herd Interaction for Yield Traits in Holsteins”. 1992 - 1994  

Languages:

Bulgarian  (mother thong).

English - read, speak, write - very good.

Russian - read, write, speak - very good.

Other skills and hobbies:

swimming, skiing, bridge.

Personal history:

Born - 23 April 1950 in Sofia.

Citizenship at birth and present - Bulgarian.

 Prof. dr George Yakimov Dimov

RESEARCH SUMMARY

Findings and areas of interest in the field of sheep breeding.

1. The influence of duration of period from lambing to first test day on yield traits of dairy sheep.

2. Dairy sheep information is summarized for over 70 000 lactations for the period of 1980-87.

3. A new trait in dairy sheep is formulated - type of lactation curve. Factors influencing it, its inheritance and connections with the other traits were the aim of an enhanced study.

Classification of the research

1. Influence of nongenetic effects on traits

      1.1. Traits

Sheep - milk yield, lactation period, maximum daily yield, persistency of lactation, type of lactation curve;

      1.2. Factors

Year, farm, herd, month of lambing, duration of first control period, type of lactation curve.

      1.3. Correction of nongenetic effects

Additive and multiplicative corrections which are performed before the genetic evaluation. (ANOVA1)

      1.4. Simplifying of milk recording in dairy sheep

Prediction of milk yield for complete lactation or commercial yield (after weaning, e.g. for milking only period) based on two test day records. Breed differences, season and age effects.

2. Genetic parameters

      2.1. Methods

      - intraclass correlation, no computers, later on Becker’s (1968) methods on CPM and DOS type machines.

      - approaches - without correction for nongenetic effects, preliminary correction, simultaneously in the model - REML (single and multiple trait).

      - correlations among estimates of variances (additive, permanent environmental and residual) show a pattern of complementarity.

3. Selection indices

      3.1. Information from several traits

      Milk yield and persistency, weights as difference from the breeding goal. Weights does not influence the index, summation of standardized rams’ EPD rank them similarly.

      3.2. Information from several relatives for sex restricted traits

Empirical regressions of several relatives on rams’ progeny testing.

Theoretical study on increasing accuracy based on own phenotype when dams’ and paternal half-sib information is accounted for.

Accuracy of preliminary (at weaning) estimation of BV for milk yield, based on descendent and co-laterals.

Combining six types of relatives in a simulation MOET - ONBS study.

4. Study on breeding values

      4.1. Non genetic effects

Correlations between rams’ EPDs for milk yield dropped down to 0.48 when complex of fixed effects differ.

      4.2. Complex estimate on several traits

For dairy and merino rams standardized EPDs were combined with relative importance of four traits e.g. a simplification of selection index.

5. Developing of computer programs

      5.1. Non genetic effects – ANOVA

Allows an arbitrary number if cross-classified effects, or one nested within the main with several linear regressions. Direct inversion of design matrix. Tested with up to 1500 levels of a factor. Results - mean, SD, min. and max. of trait and regressions, F-test, LS-estimates, R² (determination coef.), T-test.

Variant - ANOVA1. Additive correction for all/significant fixed effects; output - prepares an input file for H2RG.

      5.2. Heritability and genetic correlations - H2RG.

No restriction for sire number, allows missing data, based on Beckers’ methods, input file for selection index.

      5.3. Selection index SI.

Arbitrary number of traits.

      5.4. BLUP sires EPD program (sire model) - SOGO.

Progeny testing for unlimited number of rams and relatively small number of fixed effects.

6. Breeding programs

      6.1. Predicted generation/year genetic gain of cattle and sheep populations with different:

- proportion of population inseminated with young sires;

- number of progenies for estimation of breeding values;

- intensity of selection of young and progeny tested sires (bulls, rams).

6.2. Prediction genetic gain for buffaloes with progeny testing and with MOET without progeny testing.

SUMMARY OF SOME RESULTS

1. Types of lactation curves in dairy ewes (Dimov, 1986).

Data: One year, 394 first and 355 second lactations, 9 herds, 25 sires, minimum № daughters - 12.

Table 1. Averages

Table 2. F-test

2. Non genetic effects on rams’ EPD for milk yield (Dimov, 1992).

Data: 2214 Pleven Black-head dairy ewes, 28 herds, only first lactation, years of birth 1982-85, month of lambing - December - March, minimum 9 daughters.

I c.p. - first control period, e.g. from lambing to I test day - n.s. in the model.

Average yield - 212,7 l (SD 37.6),  Lactation period - 225.7 days (SD 26.2), Yield for standard 200 days period - 200.5 l (SD 36.5).

Terms: Residual variance (MSE) x10^-3, SD of EPD (SD epd). Assumed h² = 0.25