Abstract
Machine learning techniques provide efficient data analysis tools without mathematical derivations. Data-centric LC representations are highly demanded to use these tools for LC-related research. A novel data-oriented LC representation model using piecewise linear regression (PWLR) is presented. This representation is intended to be used directly as data for machine learning along with other associated data at an individual base. An LC is represented in vector form as a series of connected line segments and the location and number of segments are determined by the maximum residual. The critical points are determined at the rapid transit point in the LC. The Bayesian information criterion was used to choose the proper number of line segments to avoid the overfitting problem. To demonstrate the validity of the PWLR model as an LC descriptor, its approximation accuracy and representation generality were tested experimentally. The results revealed that the PWLR model is advantageous for representing the LCs of an individual or a large herd that are directly applicable to data-driven approaches.
Highlights
The lactation curve (LC) is a periodic record of daily milk production in dairy cows for a given time period
An LC is represented in feature vector form as a series of connected line segments in the piecewise linear regression (PWLR) model, and the location and number of segments are determined by the maximum residual
The largest em of group F is associated with the following two observations: (i) the Bayesian information criterion (BIC) values are relatively large compared to other groups, and (ii) the proper number of critical points is 12. These findings show that BIC works as a metric to determine the appropriate number of critical points for PWLR
Summary
The lactation curve (LC) is a periodic record of daily milk production in dairy cows for a given time period. The value of milk yield per day is usually collected during the birth cycle from delivery to dry off. It helps estimate the total milk yield of a farm and is used as primary information to monitor the health conditions of individuals. Many LC modeling studies have been conducted. Cunha et al [1] compared various empirical and mechanistic models to test the fitting performance of various LC models. Models described by Dijkstra et al [2], Wood [3], and Wilmink [4] displayed good fitting performance for the high, medium, and low milk production groups. Hossein-Zadeh [5] showed the efficiency of the models of Wood, Dijkstra, and Rook [6] in modeling productivity with a large dataset
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