Dairy cow dry matter intake (DMI) data from Australia (AU), the United Kingdom (UK) and the Netherlands (NL) were combined (1801 cows) for this study. The aim was to explore the impact on the accuracy of genomic estimated breeding values of pooling data across key reference populations. A total of 843 Australian growing heifers with records on DMI measured over 60 to 70 d at approximately 200 d of age, 359 Scottish and 599 Dutch lactating heifers with records on DMI during the first 100 d in milk were included in the data set. Genotypes were obtained using the Illumina BovineSNP50 BeadChip for European (UK+NL) cows, and Illumina High Density Bovine SNP chip for AU heifers. The AU and EU genomic data were matched on SNP-name and genotypes were compared for quality control using 40 bulls that were genotyped in both data sets. This resulted in a total of 30,949 SNPs being used in the analyses. Genomic predictions were with both single-trait and multi-trait genomic REML models, using ASReml. The accuracy of genomic prediction was evaluated in 11 single-country validation sets, and the reference set (where animals had both DMI phenotypes and genotypes) were either a reference set within AU or EU, or with a multi-country reference set consisting of all data except the validation set. When DMI was considered to be the same trait for each country, using a multi-country reference set, the accuracy of genomic prediction for DMI increased for EU and UK, but not for AU and NL. Extending to a bivariate (AU-EU) or trivariate (AU-UK-NL) model increased the accuracy of genomic prediction for DMI in all countries. The highest accuracies were estimated for all countries when data was analyzed with a trivariate model, with increases of up to 5.5% compared with a single-trait analysis with a multi-country reference set.
Individual data on activity of broilers is valuable for breeding programmes, as activity may serve as proxy for multiple health, welfare and performance indicators. However, in current husbandry systems, broilers are often kept in large groups, which makes it difficult to identify and monitor them at the individual level. Sensor technologies, such as ultra-wideband (UWB) tracking systems, might offer solutions. This paper investigated the recorded distances of an UWB tracking system that was applied to broilers, as a first step in assessing the potential of an UWB tracking system for studying individual levels of activity in broilers housed in groups. To this end, the distances moved as recorded by the UWB system were compared to distances recorded on video, using Kinovea video tracking software. There was a moderately strong positive correlation between the output of the UWB system and video tracking, although some under- and over- estimations were observed. Even though the recorded distances from the UWB system may not completely match the true distances moved, the UWB system appears to be well-suited for studying differences in activity between individual broilers when measured with the same system settings.
Before methane (CH4) emission can be mitigated with animal breeding, breath measurements have to be recorded on a large number of cows. Our aim was to estimate heritabilities for, and a genetic correlation between, CH4 recorded by GreenFeed and sniffers. Repeated records were available for CH4 production (g/cow/day) by GreenFeed and for CH4 concentration (ppm) by sniffers. The data included 24,284 GreenFeed daily means from 822 cows, 172,948 sniffer daily means from 1,800 cows, and 1,787 daily means from both devices on the same day from 75 cows. Additionally, records were averaged per week. The datasets were analyzed using bivariate animal models. The results show that CH4 emissions recorded by either device has a moderate heritability (0.18-0.37). Furthermore, the genetic correlation between weekly mean CH4 recorded by GreenFeed or by sniffers was high (0.77). This suggest that the measurements can be used in the same genetic evaluations.
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