Dairy cattle farmers' preferences for different breeding tools

Exploration of Extension Research to Promote Genetic Improvement in Cattle Production: Systematic Review

In the cattle industry, tools for genetic improvement play a crucial role in animal selection. The changing circumstances faced by farmers and the significant part agricultural extension plays in these changes must be considered. Despite progress in genetic selection tools and the push for their adoption through extension services, a disconnect persists between the development of new strategies and tools for genetic improvement and their adoption by livestock farmers. This systematic review is designed to globally investigate the methodology and outcomes of extension research aimed at advancing genetic improvement in beef cattle. Adhering to PRISMA guidelines, a search was conducted across four databases for studies published from January 2012 to June 2023. Twenty-one articles were selected and reviewed. The research design in the articles predominantly employed mixed methods, utilizing both quantitative and qualitative approaches. While social factors are acknowledged as influencers in the adoption process, the application of theories or frameworks from social sciences is still in its early stages. To successfully implement extension activities that promote the use of genetic tools in cattle for a specific production region, more participatory research is required where farmers are actively involved.

Multibreed genomic prediction using summary statistics and a breed-origin-of-alleles approach

Because of an increasing interest in crossbreeding between dairy breeds in dairy cattle herds, farmers are requesting breeding values for crossbred animals. However, genomically enhanced breeding values are difficult to predict in crossbred populations because the genetic make-up of crossbred individuals is unlikely to follow the same pattern as for purebreds. Furthermore, sharing genotype and phenotype information between breed populations are not always possible, which means that genetic merit (GM) for crossbred animals may be predicted without the information needed from some pure breeds, resulting in low prediction accuracy. This simulation study investigated the consequences of using summary statistics from single-breed genomic predictions for some or all pure breeds in two- and three-breed rotational crosses, rather than their raw data. A genomic prediction model taking into account the breed-origin of alleles (BOA) was considered. Because of a high genomic correlation between the breeds simulated (0.62-0.87), the prediction accuracies using the BOA approach were similar to a joint model, assuming homogeneous SNP effects for these breeds. Having a reference population with summary statistics available from all pure breeds and full phenotype and genotype information from crossbreds yielded almost as high prediction accuracies (0.720-0.768) as having a reference population with full information from all pure breeds and crossbreds (0.753-0.789). Lacking information from the pure breeds yielded much lower prediction accuracies (0.590-0.676). Furthermore, including crossbred animals in a combined reference population also benefitted prediction accuracies in the purebred animals, especially for the smallest breed population.

Influence of dairy farmers' knowledge on their attitudes towards breeding tools and genomic selection

Understanding farmers' attitudes towards traits is critical for developing appropriate breeding goals for dairy production. In response to a research gap in regards to the influence of farmers' knowledge of breeding tools, this study aimed to determine the effect of farmers' knowledge on their attitudes towards the use of breeding tools and traits in typical family-owned farms in Slovenia. An online questionnaire was sent to dairy farmers affiliated with Slovenian breeding associations, and 256 dairy farmers responded. The analysis was conducted in three steps. First, the basic response patterns according to the farmers' knowledge level were determined using latent class analysis. Second, farmers' attitudes towards breeding tools were assessed by 15 statements using principal component analysis. Finally, we were interested in the relationship between farmers' attitudes and knowledge about selection. The results showed that farmers had more knowledge about the benefits of genomic selection, followed by general knowledge about breeding values and the definition of genomic selection, and they had the least knowledge about the reference population. Farmers with more knowledge were statistically significantly more likely than farmers with less knowledge to have higher education, be younger, have a larger herd size, have higher milk production per cow, have the intent to increase herd size and milk quantity, and use genomically tested bulls. No significant relationship was found between belonging to a specific knowledge class and the main breed in the herd, the farmer's gender, production system, or farming in less-favoured areas. The results also show that farmers basically agree that they need written recorded performance data about a bull/cow to know exactly how good the animal is, that the genetic merit (breeding value) of bulls/cows adds to the performance of their progeny, that it is very important to maintain the breed characteristics of bulls/cows, that cooperation in being able to compare animals with other farmers is essential for improving herd performance, and that the possibilities of selecting dairy cows with genomic selection and monogenetic traits must be fully exploited, indicating a positive attitude towards genomic selection. The level of knowledge was shown to influence attitudes towards various aspects of breeding. It was found that the higher the level of knowledge, the more positive the attitude towards genetic and genomic selection, and the more negative the attitude towards traditional selection.