|Due to restrictions on feeding and management on low input farms, there are vast differences between cattle on low input and conventional farms. Therefore, variance components of the same traits recorded in low input and conventional populations might be different. Even if the variance components were different, the necessities of setting up an overall breeding goal and implementing an own breeding program in organic production system are still open to further discussion. The first objective of this study was to estimate variance components of production, reproduction and health traits measured on Brown Swiss on low input farms in Switzerland. On the other hand, breeding strategies with consideration of genomic selection on both conventional and low input farms were compared by applying stochastic simulations.
Test-day data for milk yield (MY), fat percentage (Fat%), protein percentage (Pro%), lactose percentage (Lac%), somatic cell score (SCS), and milk urea nitrogen (MUN) were available on 1,283 cows kept in 54 small low input farms. For Gaussian distributed production traits mentioned above, a multi-trait random regression animal model (RRM) was applied with days in milk (DIM) as a time-dependent covariate. In general, daily heritabilities of production traits followed the pattern as found for high input production systems. Female fertility traits including number of inseminations (NI), stillbirth (SB), calving ease (CE), calving to first service (CTFS), days open (DO), and gestation length (GL) were analyzed with parity as a time covariate. Threshold methodology was applied for the first three traits. In most of case, heritabilities of reproduction traits were lower than 0.1. A threshold-linear sire model was applied to estimate daily correlations between MY, Fat%, Pro%, SCS, MUN and the binary distributed fertility trait conception rate (CR). Pronounced antagonistic relationships between MY and CR were in the range of -0.40 to -0.80 from DIM 20 to DIM 200. Estimated genetic parameters for reproduction traits were partly different from those estimated in high input production systems.
Phenotypic records for mastitis, metritis, retained placenta, ovarian cysts and acetonemia were available from the same cows as for production and reproduction traits, while the number of cows changed to 1,247. The five health traits were defined as binary data, categorical data and longitudinal binary data respectively. Binary data recorded between days in milk -1 and 120 were analyzed by linear models as well as threshold models with probit link function. Categorical data counted the total number of diseases during the same period and the data were analyzed by linear models and Poisson mixed models respectively. The longitudinal binary data were analyzed by linear and threshold repeatability models and RRM respectively. Apart from moderate heritabilities for mastitis (0.32) and retained placenta (0.39), heritabilities were generally low for binary and categorical traits. Repeatabilities and heritabilities of longitudinal traits estimated from repeatability models were also low. The highest daily heritabilities for all health traits were found at the beginning of lactation and at the end of the defined interval. Generally, threshold models were favored by a low Bayesian information criterion except threshold RRM.
A stochastic simulation study was carried out with a focus on an application of genomic selection in dairy cattle breeding programs, to compare true breeding values (TBV) from a variety of selection schemes. Heritability of trait of interest was low (0.1) or moderate (0.3) and genomic estimated breeding value (GEBV) was imitated by the defined accuracy, which was between 0.5 and 0.9. Three breeding strategies were simulated in total, including selection of bull calves based on pedigree index, genotyped parents and genotyped bull calves themselves. A variety of scenarios were assumed within last two breeding strategies, indicating different pre-selection criteria for each strategy. Schemes of genotyping parents of the future bulls were similar with the classical young bull program, but TBV from these schemes were competitive or superior. The highest average TBV was found to be in scenarios of genotyping young male candidates. Only if the pre-fined accuracy of GEBV was greater than 0.5, TBV of the idealistic scenario, genotyping all male calves, was competitive with scenarios of genotyping pre-selected male calves based on estimated breeding values (EBV) of bull dams or the average GEBV of bull parents. Hence, genotyping young male candidates should be most suitable strategy for breeding organizations.
In the forth part of this thesis, another stochastic simulation was applied to compare TBV and inbreeding coefficients of organic breeding program designs. Basically, three breeding strategies were simulated: i) selection of sires from conventional population with consideration of genotype by environment (G x E) interactions, ii) selection of genotyped sires from the low input population for AI, iii) selection of genotyped nature service sires (NSS) in each of the organic herd. Heritabilities of the simulated traits were 0.05 and 0.3 respectively. The G x E interactions were realized by considering genetic correlations between traits of interest recorded in different environments (rg = 0.5 to 1). GEBV were generated with accuracy (rmg) between 0.5 and 1. The average TBV of the 5 best genotyped AI sires from organic environment was always higher than selection of sires from conventional population on EBV. If the selection criterion was GEBV in both environments, rg ≤ 0.80 is the general threshold favouring selection in the organic population. Genotyped NSS were competitive with selection of sires based on EBV in conventional population, only if the significant G x E interactions (rg = 0.5) was exited between two environments and accuracy of genotyped NNS was high (rmg ≥ 0.9). Inbreeding of selected sire and their progeny could be reduced when using genomic breeding program.