Simulation, prediction, and genetic analyses of daily methane emissions in dairy cattle.

This study presents an approach combining phenotypes from novel traits, deterministic equations from cattle nutrition, and stochastic simulation techniques from animal breeding to generate test-day methane emissions (MEm) of dairy cows. Data included test-day production traits (milk yield, fat percentage, protein percentage, milk urea nitrogen), conformation traits (wither height, hip width, body condition score), female fertility traits (days open, calving interval, stillbirth), and health traits (clinical mastitis) from 961 first lactation Brown Swiss cows kept on 41 low-input farms in Switzerland. Test-day MEm were predicted based on the traits from the current data set and 2 deterministic prediction equations, resulting in the traits labeled MEm1 and MEm2. Stochastic simulations were used to assign individual concentrate intake in dependency of farm-type specifications (requirement when calculating MEm2). Genetic parameters for MEm1 and MEm2 were estimated using random regression models. Predicted MEm had moderate heritabilities over lactation and ranged from 0.15 to 0.37, with highest heritabilities around DIM 100. Genetic correlations between MEm1 and MEm2 ranged between 0.91 and 0.94. Antagonistic genetic correlations in the range from 0.70 to 0.92 were found for the associations between MEm2 and milk yield. Genetic correlations between MEm with days open and with calving interval increased from 0.10 at the beginning to 0.90 at the end of lactation. Genetic relationships between MEm2 and stillbirth were negative (0 to -0.24) from the beginning to the peak phase of lactation. Positive genetic relationships in the range from 0.02 to 0.49 were found between MEm2 with clinical mastitis. Interpretation of genetic (co)variance components should also consider the limitations when using data generated by prediction equations. Prediction functions only describe that part of MEm which is dependent on the factors and effects included in the function. With high probability, there are more important effects contributing to variations of MEm that are not explained or are independent from these functions. Furthermore, autocorrelations exist between indicator traits and predicted MEm. Nevertheless, this integrative approach, combining information from dairy cattle nutrition with dairy cattle genetics, generated novel traits which are difficult to record on a large scale. The simulated data basis for MEm was used to determine the size of a cow calibration group for genomic selection. A calibration group including 2,581 cows with MEm phenotypes was competitive with conventional breeding strategies.

Implementation of genetic evaluation and mating designs for the endangered local pig breed 'Bunte Bentheimer'.

A pedigree including 1538 individuals of the endangered pig breed 'Bunte Bentheimer' and 3008 records of the fertility traits 'number of piglets born alive' (NBA) and 'number of piglets weaned' (NW) were used to i) characterize the population structure, ii) to estimate genetic (co)variance components and estimated breeding values (EBVs) and iii) to use EBVs for the application of the concept of optimal genetic contributions. The average coefficient of inbreeding increased from F = 0.103 to F = 0.121 within the two recent cohorts. Average rate of inbreeding amounted to 1.66%, which resulted in an effective population size of Ne  = 30 animals in the recent cohort. Average generation interval was 3.07 years considering the whole pedigree, and in total, only 612 sows and boars generated offspring. Estimated heritabilities for both traits NBA and NW were 0.12, and the estimated genetic correlation between both traits was 0.96. The variance component due to the service sire was higher than in commercial pig breeds, presumably due to the widespread use of natural service boars. The EBVs for NBA from 333 selection candidates (63 boars and 270 sows) were used to determine optimal genetic contributions. Based on selected animals and their optimal genetic contributions, specific mating designs were evaluated to minimize inbreeding in the next generation. Best results were achieved when using a simulated annealing algorithm and allowing artificial insemination.