70. Identifying diversity in cattle performance using Bayesian inference and a model

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Ramirez, J.F. | Puillet, Laurence | Friggens, Nicolas, N.C.

Edité par CCSD ; Wageningen Academic Publishers -

International audience. Quantitative description of individual animal performance is a key element of breeding. In addition to measurements of traits associated with productivity, dynamic-mechanistic models can be used for identifying diversity of complex traits like feed efficiency. The objective of this work was to use Bayesian inference to quantify differences in a model's 'genetic-scaling' parameters (related to resource acquisition and resource allocation) within-and between-breeds. We first describe the experimental data processing before calibration, the function used for the error calculation, and the Bayesian algorithm used for parameter inference. Two databases were used as case studies. The results showed that this approach allowed us to identify variations in the genetic-scaling parameters within-and between-breeds. This approach is a useful tool to quantify complex traits for future selection of efficient and resilient animals.

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