Optimization of reproductive management programs using lift chart analysis and cost-sensitive evaluation of classification errors.

摘要: The common practice on most commercial dairy farms is to inseminate all cows that are eligible for breeding, while ignoring (or absorbing) the costs associated with semen and labor directed toward low-fertility unlikely conceive. Modern analytical methods, such as machine learning algorithms, can be applied cow-specific explanatory variables purpose of computing probabilities success or failure upcoming insemination events. Lift chart analysis identify subsets high fertility likely conceive therefore appropriate targets (e.g., conventional artificial expensive sex-enhanced semen), well should passed over at point in time. Although a strategy might economically viable, management, environmental, financial conditions one farm differ widely from next, hence reproductive management recommendations derived tool may suboptimal specific farms. When coupled cost-sensitive evaluation misclassified correctly classified events, potentially powerful optimizing individual In present study, lift were data set consisting 54,806 events primiparous Holstein 26 Wisconsin farms, 17,197 3 where latter had more detailed information regarding health cows. first set, gains profit limiting inseminations 79 97% fertile ranged $0.44 $2.18 per cow monthly breeding period, depending days milk yield relative contemporaries. second inseminating only subset 59% conferred gain $5.21 period. These results suggest that, when used classification algorithm, enhance performance profitability programs