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Characterizing drinking behavior from reticular temperature with artificial neural networks.

A. E. Pape

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06-23-2020

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Abstract:

T13
Characterizing drinking behavior from reticular temperature with artificial neural networks.
A. E. Pape*, C. S. Ballard. William H. Miner Agricultural Research Institute Chazy, NY.

The use of reticular temperature (RT) to assess drinking behavior would be a useful tool to evaluate animal health and well-being. Our objective was to compare the performance of a temperature threshold and an artificial neural network (ANN) for identifying drinking bouts. Four commercial dairy farms were monitored from June through September, 2019. Thirty high producing (52 � 8 kg/d) focal animals were selected on each farm and administered a RT bolus. Five days of data were selected randomly for each cow, yielding 150 cow-days for each farm that were manually labeled for drinking bouts. Two algorithms were experimented with in R 3.6.2. The temperature threshold algorithm consisted of applying a threshold at a specified quantile of RT for each cow-day, with any point falling below the threshold being classified as a drinking bout. Alternatively, 10 ANN each with a single hidden layer containing 20 units used RT observations over a 70-min time window as predictors, with the label (bout vs. non-bout) for the window being the label assigned to the time point at its center. Both algorithms were evaluated by 3 metrics: (1) area under the receiver operating characteristic curve (AUROC) using time point as the unit of observation; (2) mean absolute error (MAE) of predicted number of bouts using cow-day as the unit of observation; and (3) MAE of predicted mean interbout duration, also using cow-day as the unit of observation. Of 150 cow-days for each farm, approximately 60% were used for training and approximately 40% for validation. ANN outperformed the temperature threshold on all 3 metrics and for all 4 farms (Table 1). For mean interbout duration in particular, ANN provide enough sensitivity (MAE <4 min) to monitor even mild behavioral responses to changes in management and environmental conditions.Table 1.

FarmAlgorithmAUCMAE, n boutsMAE, interbout duration (min)
ATemperature0.9830.5111.30
ANN>0.9990.081.52
BTemperature0.9860.479.69
ANN>0.9990.123.28
CTemperature0.9870.367.62
ANN0.9980.142.10
DTemperature0.9880.4710.94
ANN>0.9990.020.32

Keywords: drinking behavior, reticular temperature, neural networks.