TY - JOUR
T1 - Estimation of grass intake on pasture for dairy cows using tightly and loosely mounted di- and tri-axial accelerometers combined with bite count
AU - Oudshoorn, Frank Willem
AU - Cornou, Cecile
AU - Hellwing, Anne Louise Frydendahl
AU - Hansen, Hanne Helene
AU - Munksgaard, Lene
AU - Lund, P.
AU - Kristensen, T.
PY - 2013/11
Y1 - 2013/11
N2 - The aim of the present study was to investigate the use of accelerometer sensors to estimate grazing time. The estimated grazing time was furthermore combined with bite frequency data in order to model grass intake. Differing levels of stocking densities and grass height were used. Two field experiments were conducted: one in 2009 (EX1) using 20 Holstein cows with 7. h daily grazing and ad libitum feeding inside, and another in 2010 (EX2) using 10 Holstein cows with 7.5. h daily grazing and restricted feeding inside. For both experiments, data collected were (i) activity data measured by accelerometers, (ii) manually registered bite counts and (iii) estimation of grass intake from energy requirements. In EX2 the necessity of tight sensor fixation was tested. Head mounted accelerometers were used for estimation of grazing time, which was computed using threshold values of raw downloaded data from one axis.Loosely mounted sensors attached to and hanging from the neck collar, compared to tightly mounted sensors on the head of cows did not result in significantly different estimations of grazing time. Bite count recordings showed cow individual differences in bite frequency (ranging from 48 to 62bitesmin-1) for the same day on the same paddock. The best estimation of grass intake was for cows which were fed restricted indoors (≈30% of diet). This was modelled by using grazing time and bite frequency and resulted in prediction intervals ranging from ±1.2 to ±1.4kgDMcow-1day-1 for continuous grazing with an initial grass height of 11cm. Adding individual bite frequency per cow to the model together with the grazing time, reduced the intake prediction interval from an average of ±2.3kgDMcow-1day-1 to ±1.3kgDMcow-1day-1 in a continuous grazing system.
AB - The aim of the present study was to investigate the use of accelerometer sensors to estimate grazing time. The estimated grazing time was furthermore combined with bite frequency data in order to model grass intake. Differing levels of stocking densities and grass height were used. Two field experiments were conducted: one in 2009 (EX1) using 20 Holstein cows with 7. h daily grazing and ad libitum feeding inside, and another in 2010 (EX2) using 10 Holstein cows with 7.5. h daily grazing and restricted feeding inside. For both experiments, data collected were (i) activity data measured by accelerometers, (ii) manually registered bite counts and (iii) estimation of grass intake from energy requirements. In EX2 the necessity of tight sensor fixation was tested. Head mounted accelerometers were used for estimation of grazing time, which was computed using threshold values of raw downloaded data from one axis.Loosely mounted sensors attached to and hanging from the neck collar, compared to tightly mounted sensors on the head of cows did not result in significantly different estimations of grazing time. Bite count recordings showed cow individual differences in bite frequency (ranging from 48 to 62bitesmin-1) for the same day on the same paddock. The best estimation of grass intake was for cows which were fed restricted indoors (≈30% of diet). This was modelled by using grazing time and bite frequency and resulted in prediction intervals ranging from ±1.2 to ±1.4kgDMcow-1day-1 for continuous grazing with an initial grass height of 11cm. Adding individual bite frequency per cow to the model together with the grazing time, reduced the intake prediction interval from an average of ±2.3kgDMcow-1day-1 to ±1.3kgDMcow-1day-1 in a continuous grazing system.
U2 - 10.1016/j.compag.2013.09.013
DO - 10.1016/j.compag.2013.09.013
M3 - Journal article
SN - 0168-1699
VL - 99
SP - 227
EP - 235
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
ER -