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Analysis of Reproductive Performance of Lactating Cows on Large Dairy Farms Using Machine Learning Algorithms

D. Z. Caraviello^*, K. A. Weigel^*,1, M. Craven, D. Gianola^*, N. B. Cook, K. V. Nordlund, P. M. Fricke^* and M. C. Wiltbank^*

^* Department of Dairy Science,
Department of Biostatistics and Medical Informatics, and
School of Veterinary Medicine University of Wisconsin, Madison 53706

¹ Corresponding author: kweigel{at}wisc.edu

The fertility of lactating dairy cows is economically important, but the mean reproductive performance of Holstein cows has declined during the past 3 decades. Traits such as first-service conception rate and pregnancy status at 150 d in milk (DIM) are influenced by numerous explanatory factors common to specific farms or individual cows on these farms. Machine learning algorithms offer great flexibility with regard to problems of multicollinearity, missing values, or complex interactions among variables. The objective of this study was to use machine learning algorithms to identify factors affecting the reproductive performance of lactating Holstein cows on large dairy farms. This study used data from farms in the Alta Genetics Advantage progeny-testing program. Production and reproductive records from 153 farms were obtained from on-farm DHI-Plus, Dairy Comp 305, or PCDART herd management software. A survey regarding management, facilities, labor, nutrition, reproduction, genetic selection, climate, and milk production was completed by managers of 103 farms; body condition scores were measured by a single evaluator on 63 farms; and temperature data were obtained from nearby weather stations. The edited data consisted of 31,076 lactation records, 14,804 cows, and 317 explanatory variables for first-service conception rate and 17,587 lactation records, 9,516 cows, and 341 explanatory variables for pregnancy status at 150 DIM. An alternating decision tree algorithm for first-service conception rate classified 75.6% of records correctly and identified the frequency of hoof trimming maintenance, type of bedding in the dry cow pen, type of cow restraint system, and duration of the voluntary waiting period as key explanatory variables. An alternating decision tree algorithm for pregnancy status at 150 DIM classified 71.4% of records correctly and identified bunk space per cow, temperature for thawing semen, percentage of cows with low body condition scores, number of cows in the maternity pen, strategy for using a clean-up bull, and milk yield at first service as key factors.

Key Words: machine learning • alternating decision tree • fertility • dairy cattle

This article has been cited by other articles:

				D. Z. Caraviello, K. A. Weigel, P. M. Fricke, M. C. Wiltbank, M. J. Florent, N. B. Cook, K. V. Nordlund, N. R. Zwald, and C. L. Rawson Survey of Management Practices on Reproductive Performance of Dairy Cattle on Large US Commercial Farms J Dairy Sci, December 1, 2006; 89(12): 4723 - 4735. [Abstract] [Full Text] [PDF]