Dairy cattle farmers' preferences for different breeding tools

Clasen, Julie; Bengtsson, Christian; Källström, Helena Nordström; Strandberg, E.; Fikse, W.F.; Rydhmer, Lotta

doi:10.1016/j.animal.2021.100409

Cited by 6 publications

(8 citation statements)

References 29 publications

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“…Data collection from farmers was through surveys or interviews with the objective to capture information that allows the farmers and production systems to be characterized and to capture their attitudes and beliefs regarding the adoption of genetic and breeding tools. For instance, attitudes were collected through a series of statements that farmers could answer using a scale of agreement or disagreement [24,36,39,40]. Data collection from farmers was predominantly through surveys.…”

Section: Methods and Frameworkmentioning

confidence: 99%

“…Among the articles that consider small-or medium-scale producers as the object of study, they are focused on beef [24,25], dairy [18,26,27], dual-purpose [27][28][29] and general cattle [30][31][32][33][34][35] production systems. In contrast, large-scale production was considered in six articles (28.6%) and examined only beef [36] and dairy [37][38][39][40][41] systems.…”

Section: Location Farmer Profile and Cattle Livestockmentioning

confidence: 99%

“…For those studies that considered cattle and other livestock [31,36], only the information and results on cattle were considered for this review. As shown in Figure 2a, 38.1% (n = 8) of the articles are related to understanding the use of genetic tools by dairy farmers [18,26,[37][38][39][40][41][42]. Only 14.3% (n = 3) of the articles examine beef production [24,25,36], increasing to 61.9% when production with breeds for dual purpose (n = 3) were considered or when the authors did not make a distinction and referred just to cattle more generally (n = 7).…”

Section: Location Farmer Profile and Cattle Livestockmentioning

confidence: 99%

See 2 more Smart Citations

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

Menchon,

Manning,

Swain

et al. 2024

Animals

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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.

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Section: Methods and Frameworkmentioning

confidence: 99%

Section: Location Farmer Profile and Cattle Livestockmentioning

confidence: 99%

Section: Location Farmer Profile and Cattle Livestockmentioning

confidence: 99%

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

Menchon,

Manning,

Swain

et al. 2024

Animals

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“…Nevertheless, crossbreeding has shown positive effects, primarily on functional traits, such as fertility and health (Sørensen et al 2008 ; Shonka-Martin et al 2019 ), which can be economically beneficial in dairy herds (Clasen et al 2020 ). As a result, the interest in crossbreeding in dairy herds is increasing, and farmers request genomically enhanced breeding values (GEBVs) for crossbred animals (Clasen et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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

Clasen

Fikse

et al. 2023

Heredity

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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.

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“…Crossbreeding between dairy cattle breeds and using genomic testing to predict genomically enhanced breeding values (GEBVs) on future replacement heifers in dairy herds are becoming more attractive to farmers (Magne and Quenon, 2021;Thomasen et al, 2020), and this leads to a stronger request from dairy farmers for predicting GEBVs for crossbred dairy cattle (Clasen et al, 2021). Previous studies have shown that combining genotype and phenotype data from multiple breeds and crossbreds in a joint reference population can yield higher prediction accuracies for crossbred animals than prediction based on single breed or crossbred reference populations (e.g., Vandenplas et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

342. Breed-origin-of-alleles approach using summary statistics for multi-breed genomic prediction in dairy cattle

Clasen

Fikse

et al. 2022

Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)

Self Cite

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Systematic crossbreeding strategies between dairy cattle breeds in dairy herds are becoming more and more attractive to farmers, and this leads to a request for genomically enhanced breeding values for crossbred females in the dairy herds. Accurate genomic prediction of crossbred animals can be achieved if the genotypic and phenotypic data of the breeds involved in the crossbreeding are available to form reference populations, to estimate marker effects. However, sharing genotype and phenotype data between breed populations may be an issue due to privacy and competition. This study investigated genomic prediction of two-breed and three-breed rotational crossbred dairy cattle using summary statistics and a breed-origin of alleles model. The results indicate that the approach can yield almost as high prediction accuracies as having full information from the pure breeds.

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Dairy cattle farmers' preferences for different breeding tools

Cited by 6 publications

References 29 publications

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

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

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

342. Breed-origin-of-alleles approach using summary statistics for multi-breed genomic prediction in dairy cattle

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