Irish cattle-based decision support tools (DSTs) currently focus on breeding and identifying genetically superior parents for future generations and the appropriate bull teams to use; however, one exception being a dairy management DST which ranks dairy cows on their remaining lifetime profitability for voluntary culling. The animal’s additive genetic merit forms the basis of all genetic-based DSTs and is estimated by disentangling an observed phenotype into the additive genetic effects from the environmental effects (i.e., BLUEs) and, in doing so, estimates are generated for both. Yet, to date only the additive genetic contribution to an animal performance has been exploited. Moreover, there are clear voids in beef management genetic-based DSTs. The objectives of this thesis were therefore to: 1) characterise best linear unbiased estimated (BLUEs) and quantify the response to selection for additive and non-additive genetic merit by herd BLUEs, 2) construct the framework for a DST for predicting the expected carcass revenue for growing cattle, and 3) develop the framework for a DST to predict the expected remaining lifetime profitability of beef females to identify candidates for culling. Data used within this thesis originated from the national cattle database and the national genetic evaluations. This thesis demonstrated that the response to genetic selection varied depending on the herd BLUE and therefore potential exists for herd BLUEs to be used when tailoring breeding values and DSTs for each individual. Results also substantiate that although the carcass value of an animal is commonly predicted from their recorded breed composition, using the transaction index framework developed, the accuracy of the carcass revenue prediction doubled. This thesis also validated that when beef females were ranked on their expected lifetime profitability, the females identified for voluntary culling contributed €32 less per calving to the herd’s profitability relative to the highest ranked females.
|Publication status||Unpublished - 2020|
- Herd and animal-level management tools, Genetic evaluations