Abstract
The objective of the present study was to compare genetic gain and inbreeding coefficients of dairy cattle in organic breeding program designs by applying stochastic simulations. Evaluated breeding strategies were: (i) selecting bulls from conventional breeding programs, and taking into account genotype by environment (G×E) interactions, (ii) selecting genotyped bulls within the organic environment for artificial insemination (AI) programs and (iii) selecting genotyped natural service bulls within organic herds. The simulated conventional population comprised 148 800 cows from 2976 herds with an average herd size of 50 cows per herd, and 1200 cows were assigned to 60 organic herds. In a young bull program, selection criteria of young bulls in both production systems (conventional and organic) were either ‘conventional’ estimated breeding values (EBV) or genomic estimated breeding values (GEBV) for two traits with low (h2=0.05) and moderate heritability (h2=0.30). GEBV were calculated for different accuracies (rmg), and G×E interactions were considered by modifying originally simulated true breeding values in the range from rg=0.5 to 1.0. For both traits (h2=0.05 and 0.30) and rmg⩾0.8, genomic selection of bulls directly in the organic population and using selected bulls via AI revealed higher genetic gain than selecting young bulls in the larger conventional population based on EBV; also without the existence of G×E interactions. Only for pronounced G×E interactions (rg=0.5), and for highly accurate GEBV for natural service bulls (rmg>0.9), results suggests the use of genotyped organic natural service bulls instead of implementing an AI program. Inbreeding coefficients of selected bulls and their offspring were generally lower when basing selection decisions for young bulls on GEBV compared with selection strategies based on pedigree indices.
Highlights
Population structure, breeding strategies and breeding goals might differ in organic production systems
Average true breeding values (TBV) from scenario O_GEBV_AI for rmg = 0.7 was even higher compared with a young bull program in the conventional population, that is when neglecting G × E interactions
For making decisions on the optimal strategy, crucial parameters are genetic correlations in the trait of interest measured in different production systems, and accuracies of genomic estimated breeding values (GEBV)
Summary
Population structure, breeding strategies and breeding goals might differ in organic production systems. For organic or low input dairy cattle farming, new functional health traits play an important role (Rozzi et al, 2007). Due to the comparatively small population size, implementation of an own or independent organic progeny testing program for novel traits is associated with reduced genetic gain and with negative impact on. Organic dairy cattle farmers have continued with the use of semen from progeny tested sires or from young bulls from conventional dairy cattle breeding programs. Only a few routinely recorded indicator traits are used to improve dairy cattle health by breeding. Somatic cell count (SCC) has been used in conventional breeding programs as an indicator trait for mastitis over decades, but genetic correlations between somatic cell score and clinical mastitis only range between 0.60 and 0.70 (e.g. Emanuelson et al, 1988)
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