Deriving intake from multiple 3-dimensional accelerations in peripartal Holstein dairy cows.

Document Type

Abstract

Publication Date

2019

Location

2019 American Dairy Science Association Annual Meeting: Cincinnati, Ohio

Publisher

American Dairy Science Association

Journal

Journal of Dairy Science

Volume

102

Issue

Suppl.1

Pages

94

Language

en.

Keywords

ccelerometer, intake, sensor technology

Abstract

Dry matter intake is commonly observed to decreased around calving in dairy cows and this can have severe health consequence depending how acute and prolonged this effect occurs. The objective was to evaluate the use of 3-dimensional accelerometer sensors to estimate DMI in peripartal dairy cows. Twelve Holstein dairy cows housed in bedded pack pens during close-up were fitted with 3 sensors that record acceleration in the 3-axis (i.e., x, y, and z), one sensor on the lateral side of the left hind leg and 2 attached to a halter directly superpose over the jaw and nose. After calving, cows were moved to a free-stall barn bedded with straw. Cows were assigned 2 groups, a data collection group (A; n = 6) and a validation group (B; n = 6). Accelerations and individual intakes were collected from −7 to 7 d relative to parturition. Cows were trained to use Calan gates at least 7 d before data collection. Sensors were set to record the accelerations at 1 min intervals. Acceleration characteristics highly associated with DMI determine in a previous study were used to crossreference accelerometer data and DMI in group A. Six new variables were derived based on lag-times in jaw and nose accelerations. The REG procedure of SAS was used in group B derive DMI from acceleration combinations (DMIA) and compared this against the actual DMI using the CORR and MIXED procedures of SAS. Previously, 921 acceleration combinations were deemed relevant for DMI estimation, and these were tested in the current study. LegX+NoseX model had the strongest positive correlation (r = 0.54), and its DMIA was similar (P = 0.26) than the actual DMI (14.5 vs 15.9 kg/d), but it did not follow the rapid decreased observed in actual DMI around calving (P = 0.07). In contrast, LegZ+JawX+LagNoseZ+LagNoseX model had the closest actual DMI estimation (P = 0.99), but a not significant correlation (P = 0.46). The intermediate model LegZ+JawZ+JawY+LagNoseY+LagJawY had a weak correlation (r = 0.30) but described a similar decreased (P = 0.53) between DMIA and actual DMI around calving. Accelerometer sensors have a great potential to estimate peripartal DMI, could be a future approach utilize by commercial dairy farms to flag cows at risk to develop a postpartal disease.

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