A Segmented Method to Predict Lifetime Productivity and Optimise Culling Across Age Classes in Commercial Sheep Flocks

Abstract

<p>Opportunities for increasing productivity and profitability within the sheep industry are constantly increasing with advances in genetic, measurement and management technologies. The challenge is for producers to find the key opportunities for their own production system and identify which are the most beneficial in their environment and to meet their target markets. Available decision support tools for replacement and culling decisions in Merino sheep flocks are limited to single selection events and managing whole age classes until culling at a fixed age.</p> <p>A method was developed to predict lifetime production changes in cohorts of animals in response to various strategies for selection and culling across multiple age classes. Simple deterministic predictions assume selection from normally distributed data, which would not work in scenarios where selection has already occurred and the distribution of animals within selected cohorts is not normal. Therefore, a new deterministic approach was developed by predicting from segments of phenotypes within a distribution and then combining the outcomes across overlapping segments for whole flock or cohort outcomes. This enables selection from skewed distributions of animals, as a result of previous culling or selection events. Deterministic prediction was preferred to stochastic simulation because it provided a single, repeatable outcome for each specific strategy. This provides an ideal platform for applying an optimisation algorithm for efficiently finding the best strategy among numerous possibilities to meet an objective. For this purpose we used a Differential Evolution algorithm. The importance of applying such an algorithm increases as the complexity of possible combinations contributing to the objective increases, such as multiple factors influencing the value of an economic objective.</p> <p>This so-called “segmented method” was initially developed for prediction of continuous traits, but was unsuitable for predicting discrete categorical data, such as reproduction. Therefore, a method for examining discrete traits on the same platform was also developed, where the traits were modelled using an underlying continuous liability scale with thresholds between lambing subclasses to determine response in reproduction in each age class. By integrating these adjustments for discrete traits into the segmented method, responses in discrete traits could also be predicted from skewed distributions as a result of selection across multiple age classes.</p> <p>This thesis describes the development of this new prediction method and the associated optimising algorithm using examples for increasing productivity within Merino flocks, specifically optimising wool profitability or increasing reproduction performance. This method provides opportunity to better understand consequences of selection and management decisions on cohorts of the flock at different age classes and the population as a whole. The method developed also provides a base upon which to add more components (such as additional traits, targeted management applications or treatment options) that will act as a stimulus for researchers and extension staff to ask further questions on the potential value of future information sources, as well as to identify better ways of using currently available information. Although this study has focussed on the benefits of application within a commercial Merino flock the method is applicable to all livestock industries by replacing relevant parameters and altering objectives.</p>