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A method of using a genetic algorithm to examine the optimum structure of the Australian sheep breeding industry: open nucleus breeding systems, MOET and AI

Citation

Horton, BJ, A method of using a genetic algorithm to examine the optimum structure of the Australian sheep breeding industry: open nucleus breeding systems, MOET and AI, Australian Journal of Experimental Agriculture, 36 pp. 249-258. ISSN 0816-1089 (1996) [Refereed Article]

DOI: doi:10.1071/EA9960249

Abstract

A genetic algorithm was evaluated as a means of using a computer model to determine the optimum structure of the Australian sheep breeding industry. The model simulates an open-nucleus 3-tiered sheep breeding system to investigate the benefit of strategies such as multiple ovulation and embryo transfer (MOET) and artificial insemination (AI) in terms of both genetic improvement and dollar values. The model indicated that both MOET and non-MOET systems benefit from an open nucleus, and both could also benefit from the promotion of some ewes from the commercial flocks. However, MOET systems require a relatively large number of rams, whereas non-MOET systems should have a smaller number of rams despite the much larger number of ewes needed. Under the conditions studied here, there was only limited advantage of MOET over non-MOET schemes. The optimum structure of the MOET, non-MOET and closed systems differ substantially. A genetic algorithm is a simple method for considering a single change (such as reproductive rate, or open v. closed) that may require major changes in the industry structure to achieve the full benefits. The method could also be of value in many other situations requiring optimisation of complex models.

Item Details

Item Type:Refereed Article
Research Division:Agricultural and Veterinary Sciences
Research Group:Animal Production
Research Field:Animal Protection (Pests and Pathogens)
Objective Division:Animal Production and Animal Primary Products
Objective Group:Livestock Raising
Objective Field:Sheep - Wool
UTAS Author:Horton, BJ (Dr Brian Horton)
ID Code:69873
Year Published:1996
Web of Science® Times Cited:12
Deposited By:Agricultural Science
Deposited On:2011-05-23
Last Modified:2011-05-23
Downloads:0

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