Use of decision science to aid selection of genetically superior animals : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Science at Massey University, Palmerston North, New Zealand
| dc.contributor.author | Sherriff, Ryan Leith | |
| dc.date.accessioned | 2010-05-02T22:51:45Z | |
| dc.date.available | NO_RESTRICTION | en_US |
| dc.date.available | 2010-05-02T22:51:45Z | |
| dc.date.issued | 2010 | |
| dc.description.abstract | This thesis is concerned with a theoretical simulation model for pig breeding, as part of the ongoing search for the “perfect” genotype. The starting point is an additive model to investigate how accurately the classical, infinitesimal model predicts genetic gain for traits controlled by few loci and few alleles. This initial investigation demonstrates that the infinitesimal model is robust, providing that at least 15 loci are controlling a trait and there is symmetry in the allele distributions. A Genotype-Pig (GE-Pig) model is then developed to apply the additive effects of alleles on sub-phenotypic traits like maximum protein deposition, minimum lipid to protein content in the whole body, ad libitum digestible energy intake, energy for maintenance requirement and water content in the whole body. These parameters are then used in a nutrient partitioning simulation model to growth a pig and calculate traditional breeding traits such as average daily gain, feed conversion ratio, and backfat thickness for any combination of alleles. Three algorithms, Genetic Algorithm, Tabu Search, and Simulated Annealing, are used to investigate the GE-Pig model and find optimal combination of alleles for different dietary and selection objective situations. The two diets investigated were either of a low or high quality, and the three selection objectives used were, maximising average daily gain, minimizing feed conversion ratio, and minimizing back fat. A graphical method is developed for easy comparison of the genotypes. Of the algorithms, the Genetic Algorithm performed the best, followed by Tabu Search and finally Simulated Annealing. It is demonstrated that, in general, there is a different, single, optimum for any given selection objective and diet. However under the back fat selection objective, both diets produce the same optimal genotype. Also there are many similarities between the optima for the average daily gain and feed conversion ratio selection objectives. When the theoretical minimum number of generations of selection to the optima is considered, the feed conversion ratio selection objective is the quickest for a breeding program to achieve the optimal solutions, followed by back fat, then average daily gain. It is demonstrated that diet also has an effect on the theoretical number of generations. A Multiple selection objective, using relative economic values applied to the individual selection objectives, is also investigated. For both diets, the majority of the multiple selection objective solutions are in the vicinity of the feed conversion ratio optima, indicating that feed conversion ratio is the most prominent factor. It is also demonstrated that the optimal solution is most affected by the objective parameter weights under low diet conditions. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10179/1277 | |
| dc.language.iso | en | en_US |
| dc.publisher | Massey University | en_US |
| dc.rights | The Author | en_US |
| dc.subject | Swine breeding | en_US |
| dc.subject | Pig breeding | en_US |
| dc.subject | Decision science | en_US |
| dc.subject.other | Fields of Research::300000 Agricultural, Veterinary and Environmental Sciences::300400 Animal Production::300401 Animal breeding | en_US |
| dc.title | Use of decision science to aid selection of genetically superior animals : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Science at Massey University, Palmerston North, New Zealand | en_US |
| dc.type | Thesis | en_US |
| massey.contributor.author | Sherriff, Ryan Leith | |
| thesis.degree.discipline | Animal Science | en_US |
| thesis.degree.grantor | Massey University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) | en_US |
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