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

Area of Science:

• Dairy Science
• Animal Reproduction
• Machine Learning Applications

Background:

• Commercial dairy farms typically inseminate all eligible cows, incurring costs for low-fertility animals.
• Predictive analytics can identify cows with higher conception probabilities, optimizing insemination strategies.

Purpose of the Study:

• To evaluate the economic viability of using lift chart analysis and cost-sensitive evaluation for optimizing dairy cow insemination.
• To assess the impact of targeted insemination strategies on farm profitability.

Main Methods:

• Applied lift chart analysis and cost-sensitive evaluation to large datasets of Holstein cow insemination events.
• Utilized machine learning algorithms to compute probabilities of insemination success based on cow-specific variables.
• Analyzed two datasets: 54,806 inseminations across 26 farms and 17,197 inseminations across 3 farms with detailed health data.

Main Results:

• In the first dataset, limiting inseminations to 79-97% of fertile cows yielded profit gains of $0.44 to $2.18 per cow monthly.
• In the second dataset, inseminating only 59% of the most fertile cows resulted in a profit gain of $5.21 per cow monthly.
• Profitability varied based on factors like days in milk and milk yield relative to contemporaries.

Conclusions:

• Lift chart analysis combined with cost-sensitive evaluation can significantly enhance the performance and profitability of dairy farm reproductive management.
• Targeted insemination strategies, guided by predictive analytics, offer a powerful tool for optimizing resource allocation and increasing farm revenue.
• This approach allows for tailored reproductive management recommendations adaptable to individual farm conditions.