Type of Document Master's Thesis Author Wilson II, Lawriston A URN etd-461113689652491 Title Prediction of Fertility of Virginia Beef Heifers Using Expert Systems Technology Degree Master of Science Department Animal and Poultry Sciences Advisory Committee
Advisor Name Title Beal, W. E. Heatwole, Conrad D. Hohenboken, William D. Committee Chair Keywords
- artificial intelligence
- beef cattle
- biological type
Date of Defense 1996-08-16 Availability unrestricted AbstractAn expert system to predict the fertility of beef heifers was developed using the A.I. Toolkit KAPPA-PC
2.33. The knowledge base was developed from scientific literature and from a beef cattle reproduction
expert. The expert system computes an evaluation age, age both at the start and end of the breeding season,
and weight per day of age to classify a heifer as having either a 3LOW2, 3GOOD2, or 3EXCELLENT2
likelihood of conception. The expert system summarizes the information that is entered into the computer and
creates a text file of the summary. It also gives explanations for every prediction to help identify and alleviate
any problem areas that may affect a heifer1s ability to reproduce. The program requires an IBM compatible
computer installed with Windows 3.13 or greater. From simulated data for purebred or crossbred British
cattle, there was 72% prediction agreement between the expert system and the expert. From analysis of
historical data of Hereford-Angus crossbred cattle, heifers categorized as 3LOW2 and 3GOOD2 had
significantly higher observed pregnancy rates than expected for each category. There was no significant
difference between observed and expected pregnancy rates for heifers in the 3EXCELLENT2 category.
Pregnancy rates for post-weaning and pre-breeding evaluations for the 3LOW2 heifers were found to be
lower from the combined 3GOOD2 and 3EXCELLENT2 heifers at P=.03 and P=.06 respectively. Observed
successful calving rates for heifers categorized as 3LOW2, 3GOOD2, and 3EXCELLENT2 did not differ
significantly from the expected calving rates for each category.
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