Phenotypic and Genome-Enabled Prediction of Reproductive Performance in Dairy Cattle Using Machine Learning Algorithms.

摘要

Fast and cost-effective prediction models are increasingly in demand for commercial use. Prediction of the outcomes of insemination events as successes or failures based on explanatory variables related to genetic predisposition, health history, and lactation performance can have an impact on decision-making on dairy farms. However, interactions between management and physiological features are very complex. Machine learning algorithms can be useful for understanding these complex interactions and developing tools that will help farmers make accurate reproductive management decisions. Results of this study showed that random forests have the best performance in predicting the outcome of an insemination event and that health records of the cow are very important in this prediction. Optimizing classification rate without taking into account the cost of classification errors can be misleading. Nevertheless, the cost of not breeding a cow that would have conceived is much higher than the cost of breeding a cow that would not conceive. The common practice on most commercial dairy farms is to inseminate all cows that are eligible for breeding, which is debatable.;In conjunction with a lift chart analysis, which guides selection of subsets of highly or lowly fertile animals with highest and lowest probabilities of conception, the approach described herein could successfully stratify the pool of eligible cows in order to use different breeding strategies or use semen with different prices in different subsets of eligible cows in order to maximize total economic gain, as well as profit per eligible cow. This approach can enhance profitability of the dairy farm if sufficient data regarding variables that affect insemination outcomes are available.;Fuzzy expert systems are distinguished from other black boxed non-parametric methods, such as random forests and artificial neural networks, because they are easy to understand and interpret. There is lack of research on rule-based methods for genomic selection, because knowledge acquisition in such a complex and highly dimensional space is a limiting factor. In this dissertation, a hybrid fuzzy expert system, which uses genetic algorithms and particle swarm optimization as knowledge acquisition tools from the data was introduced for prediction of daughter pregnancy rate in Holstein bulls.