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

Abstract

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

A stochastic dynamic model of a dairy farm to evaluate the technical and economic performance under different scenarios

“…Models can be used (a) to take decisions in complex situations where many variables interact dynamically over time; (b) to analyze the effect of future circumstances on farm performance and profitability; (c) to assess risks and uncertainties through sensitivity analysis; and (d) to make decisions about areas in which little information is available. Many different areas of the dairy production system have already been modeled, such as diseases (Østergaard et al, 2005;Bruijins et al, 2010), reproductive strategies (Giordano et al, 2011(Giordano et al, , 2012Shahinfar et al, 2015), replacement policies (Cabrera, 2010), feeding strategies (Cabrera and Kalantari, 2016), and interactions between genotype and environment (Bryant et al, 2005;Kaniyamattam et al, 2016), among others. However, in most cases, these models are limited to simulate the consequences of 1 specific problem and do not account for the potential interactions with other areas of the farm.…”

A prognostic model to predict the success of artificial insemination in dairy cows based on readily available data

“…Nevertheless, BCS and milk production provide proxies of underlying diseases that affect fertility in dairy cows. Diseases are known to influence insemination success (Inchaisri et al, 2010a;Shahinfar et al, 2015).…”

“…These decisions could be economically optimized in a decision support model, such as the model developed by Steeneveld and Hogeveen (2012). Previous work has indicated that the VWP (Inchaisri et al, 2011b), the decision to stop inseminating cows (Inchaisri et al, 2012), and inseminating only a proportion of the herd with the highest chance on insemination success could be optimized economically (Shahinfar et al, 2015). Therefore, the potential exists for economi-cally optimized decision support systems for insemination decisions, which would need a prognostic model like the current model.…”

The utility of sensor technology to support reproductive management on dairy farms

“…These decisions could be economically optimized in a decision support model, such as the model developed by . Previous work has indicated that the VWP (Inchaisri, et al, 2011b), the decision to stop inseminating cows (Inchaisri, et al, 2012) and inseminating only a proportion of the herd with the highest chance on insemination success could be optimized economically (Shahinfar, et al, 2015). Therefore, potential for economically optimized decision support systems for insemination decisions, which would need a prognostic model like the current model exists.…”

“…Studies have been conducted on the economic benefits of improved reproductive performance (Galvão, et al, 2013, Giordano, et al, 2011, Inchaisri, et al, 2010b, Shahinfar, et al, 2015. Firk et al (2002) discussed the use of sensors for estrus detection, with a focus on detection performance and algorithms used.…”