Early prediction of respiratory disease in preweaning dairy calves using feeding and activity behaviors

J M Bowen*, MJ Haskell, G A Miller, CS Mason, DB Bell, C-A Duthie

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Bovine respiratory disease (BRD) represents one of the major disease challenges affecting preweaning dairy-bred calves. Previous studies have shown that differences in feeding and activity behaviors exist between healthy and diseased calves affected by BRD. The aim of this study was to develop and assess the accuracy of models designed to predict BRD from feeding and activity behaviors. Feeding and activity behaviors were recorded for 100 male preweaning calves between ~8 to 42 d of age. Calves were group housed with ad libitum access to milk via automatic milk feeders, water, starter diet, and straw. Activity was monitored via a leg-mounted accelerometer. Health status of individual calves was monitored daily using an adapted version of the Wisconsin Scoring System to identify BRD. Three models were created to predict disease: (1) deviation from normal lying time based on moving averages (MA); (2) random forest (RF), a machine learning technique based on feeding and activity variables; and (3) a combination of RF and MA output. For the MA model, lying time was predicted based on behavior over previous days (3- and 7-d MA) and the expected value for the current day (based on calf age; measured using accelerometers). Data were not split into training and test data sets. Occasions when the actual lying time increased >9% of predicted lying time were classified as a deviation from normal and a disease alert was provided. Both feeding and activity behaviors were included within the RF model. Data were split into training (70%) and test (30%) data sets based on disease events. Events were classified as 2 d before, the day(s) of the disease event, and 2 d after the event. Accuracy of models was assessed using sensitivity, specificity, balanced accuracy, and Matthews correlation coefficient (MCC). If a positive disease prediction agreed with an actual disease event within a 3-d rolling window, it was classified as a true positive. Stand-alone models (RF; MA) showed high specificity (0.95; 0.97), moderate sensitivity (0.35; 0.43), balanced accuracy (0.65; 0.64), and MCC (0.25; 0.29). Combining outputs increased accuracy (specificity = 0.95, sensitivity = 0.54, balanced accuracy = 0.75, MCC = 0.36). The work presented is the first to demonstrate the use of modeling data derived from precision livestock farming techniques that monitor feeding and activity behaviors for early detection of BRD in preweaning calves, offering a significant advance in health management of youngstock.
Original languageEnglish
JournalJournal of Dairy Science
Volume104
Early online date25 Aug 2021
DOIs
Publication statusFirst published - 25 Aug 2021

Keywords

  • behavior
  • bovine respiratory disease
  • disease prediction
  • precision livestock farming

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