Use of sensor-determined behaviours to develop algorithms for pasture intake by individual grazing cattle
P. L. Greenwood A B F , D. R. Paull B , J. McNally B , T. Kalinowski B , D. Ebert A , B. Little C , D. V. Smith D , A. Rahman D , P. Valencia E , A. B. Ingham C and G. J. Bishop-Hurley C
+ Author Affiliations
- Author Affiliations
A NSW Department of Primary Industries, Beef Industry Centre, University of New England, Armidale, NSW 2351, Australia.
B CSIRO Agriculture and Food, FD McMaster Research Laboratory Chiswick, Armidale, NSW 2350, Australia.
C CSIRO Agriculture and Food, Queensland Biosciences Precinct, St Lucia, Qld 4067, Australia.
D CSIRO Data61, Sandy Bay, Tas. 7005, Australia.
E CSIRO Data61, Pullenvale, Qld 4069, Australia.
F Corresponding author. Email: paul.greenwood@dpi.nsw.gov.au
Crop and Pasture Science 68(12) 1091-1099 https://doi.org/10.1071/CP16383
Submitted: 18 October 2016 Accepted: 16 March 2017 Published: 13 April 2017
Abstract
Practical and reliable measurement of pasture intake by individual animals will enable improved precision in livestock and pasture management, provide input data for prediction and simulation models, and allow animals to be ranked on grazing efficiency for genetic improvement. In this study, we assessed whether pasture intake of individual grazing cattle could be estimated from time spent exhibiting behaviours as determined from data generated by on-animal sensor devices. Variation in pasture intake was created by providing Angus steers (n = 10, mean ± s.d. liveweight 650 ± 77 kg) with differing amounts of concentrate supplementation during grazing within individual ryegrass plots (≤0.22 ha). Pasture dry matter intake (DMI) for the steers was estimated from the slope (kg DM day–1) of the regression of total pasture DM per plot on intake over an 11-day period. Pasture DM in each plot, commencing with ≤2 t DM ha–1, was determined by using repeatedly calibrated pasture height and electronic rising plate meters. The amounts of time spent grazing, ruminating, walking and resting were determined for the 10 steers by using data from collar-mounted, inertial measurement units and a previously developed, highly accurate, behaviour classification model. An initial pasture intake algorithm was established for time spent grazing: pasture DMI (kg day–1) = –4.13 + 2.325 × hours spent grazing (P = 0.010, r2 = 0.53, RSD = 1.65 kg DM day–1). Intake algorithms require further development, validation and refinement under varying pasture conditions by using sensor devices to determine specific pasture intake behaviours coupled with established methods for measuring pasture characteristics and grazing intake and selectivity.
Additional keywords: accelerometer, acoustic sensor, magnetometer, GPS, herbivores.
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